Publications by authors named "Babak A Ardekani"

60 Publications

Effect of citalopram on hippocampal volume in first-episode schizophrenia: Structural MRI results from the DECIFER trial.

Psychiatry Res Neuroimaging 2021 Apr 7;312:111286. Epub 2021 Apr 7.

Department of Psychiatry, NYU Langone Health, 1 Park Avenue, New York, NY 10016, United States of America; Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, United States of America. Electronic address:

Hippocampal volume loss is prominent in first episode schizophrenia (FES) and has been associated with poor clinical outcomes and with BDNF genotype; antidepressants are believed to reverse hippocampal volume loss via release of BDNF. In a 12-month, placebo-controlled add-on trial of the antidepressant, citalopram, during the maintenance phase of FES, negative symptoms were improved with citalopram. We now report results of structural brain imaging at baseline and 6 months in 63 FES patients (34 in citalopram group) from the trial to assess whether protection against hippocampal volume loss contributed to improved negative symptoms with citalopram. Hippocampal volumetric integrity (HVI) did not change significantly in the citalopram or placebo group and did not differ between treatment groups, whereas citalopram was associated with greater volume loss of the right CA1 subfield. Change in cortical thickness was associated with SANS change in 4 regions (left rostral anterior cingulate, right frontal pole, right cuneus, and right transverse temporal) but none differed between treatment groups. Our findings suggest that minimal hippocampal volume loss occurs after stabilization on antipsychotic treatment and that citalopram's potential benefit for negative symptoms is unlikely to result from protection against hippocampal volume loss or cortical thinning.
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http://dx.doi.org/10.1016/j.pscychresns.2021.111286DOI Listing
April 2021

Association of Aripiprazole With Reduced Hippocampal Atrophy During Maintenance Treatment of First-Episode Schizophrenia.

J Clin Psychopharmacol 2021 May-Jun 01;41(3):244-249

Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai.

Purpose/background: Hippocampal volume loss in early schizophrenia has been linked with markers of inflammation and oxidative stress, and with less response of negative symptoms. Aripiprazole has been reported to preserve hippocampal volume and to reduce inflammation.

Methods/procedures: Study 1 was a 12-month multicenter randomized placebo-controlled trial of citalopram added to clinician-determined second-generation antipsychotic medication in 95 patients with first-episode schizophrenia (FES), 19 of whom received aripiprazole. We compared participants taking aripiprazole with those on other antipsychotics to determine whether those on aripiprazole had less hippocampal volume loss. We also examined peripheral biomarker data from medication-naive patients with schizophrenia receiving 8 weeks of antipsychotic treatment (n = 24) to see whether markers of inflammation and oxidative stress that previously predicted hippocampal volume differed between aripiprazole (n = 9) and other antipsychotics (study 2).

Findings/results: Aripiprazole was associated with a mean increase in hippocampal volume of 0.35% (SD, 0.80%) compared with a 0.53% decrease (SD, 1.2%) with other antipsychotics during the first year of maintenance treatment in patients with FES. This difference was significant after adjusting for age, sex, citalopram treatment, and baseline Brief Psychiatric Rating Scale score (B = 0.0079, P = 0.03). Aripiprazole was also associated with reduced concentrations of the inflammatory cytokines interleukin-8 and tumor necrosis factor (P < 0.01) during the first 8 weeks of treatment in medication-naive patients with FES.

Implications/conclusions: These results suggest that aripiprazole may protect against hippocampal atrophy via an anti-inflammatory mechanism, but these results require replication in larger, randomized trials, and the clinical relevance of hippocampal volume loss is not established.
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http://dx.doi.org/10.1097/JCP.0000000000001391DOI Listing
April 2021

Effects of sex, age, and apolipoprotein E genotype on hippocampal parenchymal fraction in cognitively normal older adults.

Psychiatry Res Neuroimaging 2020 07 14;301:111107. Epub 2020 May 14.

Center for Brain Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.

Early detection of Alzheimer's disease (AD) is important for timely interventions and developing new treatments. Hippocampus atrophy is an early biomarker of AD. The hippocampal parenchymal fraction (HPF) is a promising measure of hippocampal structural integrity computed from structural MRI. It is important to characterize the dependence of HPF on covariates such as age and sex in the normal population to enhance its utility as a disease biomarker. We measured the HPF in 4239 structural MRI scans from 340 cognitively normal (CN) subjects aged 59-89 years from the AD Neuroimaging Initiative database, and studied its dependence on age, sex, apolipoprotein E (APOE) genotype, brain hemisphere, intracranial volume (ICV), and education using a linear mixed-effects model. In this CN cohort, HPF was inversely associated with ICV; was greater on the right hemisphere compared to left in both sexes with the degree of right > left asymmetry being slightly more pronounced in men; declined quadratically with age and faster in APOE ϵ4 carriers compared to non-carriers; and was significantly associated with cognitive ability. Consideration of HPF as an AD biomarker should be in conjunction with other subject attributes that are shown in this research to influence HPF levels in CN older individuals.
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http://dx.doi.org/10.1016/j.pscychresns.2020.111107DOI Listing
July 2020

Air pollution and hippocampal atrophy in first episode schizophrenia.

Schizophr Res 2020 04 10;218:63-69. Epub 2020 Mar 10.

NYU Langone Health Department of Psychiatry, New York, NY, United States of America; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States of America. Electronic address:

Air pollution has recently been linked to central nervous system (CNS) diseases, possibly mediated by inflammation and oxidative stress. Hippocampal atrophy in individuals with first episode schizophrenia (FES) has also been associated with biomarkers of inflammation and oxidative stress, whereas hippocampal atrophy was not observed in matched healthy controls with similar biomarker levels of inflammation and oxidative stress. Fine particulate matter (PM2.5), one component of air pollution, is most strongly implicated in CNS disease. The present study examined the association between PM2.5 and hippocampal volume in individuals with FES who participated in a 52-week placebo-controlled clinical trial of citalopram added to clinician-determined antipsychotic treatment at four sites in the US and China. Left hippocampal volumetric integrity (LHVI; inversely related to atrophy) was measured at baseline and week 52 using an automated highly-reliable algorithm. Mean annual PM2.5 concentrations were obtained from records compiled by the World Health Organization. The relationships between baseline LHVI and PM2.5 and change in LHVI and PM2.5 were evaluated using regression analyses. 89 participants completed imaging at baseline and 46 participants completed imaging at week 52. Mean annual PM2.5 was significantly associated with both baseline LHVI and change in LHVI after controlling for age, sex, baseline LHVI, duration of untreated psychosis and baseline antipsychotic medication dose. Air pollution may contribute to the progression of hippocampal atrophy after a first episode of illness, but these findings should be considered preliminary since other unmeasured factors may have differed between cities and contributed to the observed effect.
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http://dx.doi.org/10.1016/j.schres.2020.03.001DOI Listing
April 2020

Random Forest Classification of Alcohol Use Disorder Using EEG Source Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures.

Behav Sci (Basel) 2020 Mar 1;10(3). Epub 2020 Mar 1.

Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA.

Individuals with alcohol use disorder (AUD) manifest a variety of impairments that can be attributed to alterations in specific brain networks. The current study aims to identify features of EEG-based functional connectivity, neuropsychological performance, and impulsivity that can classify individuals with AUD (N = 30) from unaffected controls (CTL, N = 30) using random forest classification. The features included were: (i) EEG source functional connectivity (FC) of the default mode network (DMN) derived using eLORETA algorithm, (ii) neuropsychological scores from the Tower of London test (TOLT) and the visual span test (VST), and (iii) impulsivity factors from the Barratt impulsiveness scale (BIS). The random forest model achieved a classification accuracy of 80% and identified 29 FC connections (among 66 connections per frequency band), 3 neuropsychological variables from VST (total number of correctly performed trials in forward and backward sequences and average time for correct trials in forward sequence) and all four impulsivity scores (motor, non-planning, attentional, and total) as significantly contributing to classifying individuals as either AUD or CTL. Although there was a significant age difference between the groups, most of the top variables that contributed to the classification were not significantly correlated with age. The AUD group showed a predominant pattern of hyperconnectivity among 25 of 29 significant connections, indicating aberrant network functioning during resting state suggestive of neural hyperexcitability and impulsivity. Further, parahippocampal hyperconnectivity with other DMN regions was identified as a major hub region dysregulated in AUD (13 connections overall), possibly due to neural damage from chronic drinking, which may give rise to cognitive impairments, including memory deficits and blackouts. Furthermore, hypoconnectivity observed in four connections (prefrontal nodes connecting posterior right-hemispheric regions) may indicate a weaker or fractured prefrontal connectivity with other regions, which may be related to impaired higher cognitive functions. The AUD group also showed poorer memory performance on the VST task and increased impulsivity in all factors compared to controls. Features from all three domains had significant associations with one another. These results indicate that dysregulated neural connectivity across the DMN regions, especially relating to hyperconnected parahippocampal hub as well as hypoconnected prefrontal hub, may potentially represent neurophysiological biomarkers of AUD, while poor visual memory performance and heightened impulsivity may serve as cognitive-behavioral indices of AUD.
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http://dx.doi.org/10.3390/bs10030062DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7139327PMC
March 2020

Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures.

Brain Sci 2020 Feb 20;10(2). Epub 2020 Feb 20.

Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA.

Individuals with alcohol use disorder (AUD) are known to manifest a variety of neurocognitive impairments that can be attributed to alterations in specific brain networks. The current study aims to identify specific features of brain connectivity, neuropsychological performance, and impulsivity traits that can classify adult males with AUD ( = 30) from healthy controls (CTL, = 30) using the Random Forest (RF) classification method. The predictor variables were: (i) fMRI-based within-network functional connectivity (FC) of the Default Mode Network (DMN), (ii) neuropsychological scores from the Tower of London Test (TOLT), and the Visual Span Test (VST), and (iii) impulsivity factors from the Barratt Impulsiveness Scale (BIS). The RF model, with a classification accuracy of 76.67%, identified fourteen DMN connections, two neuropsychological variables (memory span and total correct scores of the forward condition of the VST), and all impulsivity factors as significantly important for classifying participants into either the AUD or CTL group. Specifically, the AUD group manifested hyperconnectivity across the bilateral anterior cingulate cortex and the prefrontal cortex as well as between the bilateral posterior cingulate cortex and the left inferior parietal lobule, while showing hypoconnectivity in long-range anterior-posterior and interhemispheric long-range connections. Individuals with AUD also showed poorer memory performance and increased impulsivity compared to CTL individuals. Furthermore, there were significant associations among FC, impulsivity, neuropsychological performance, and AUD status. These results confirm the previous findings that alterations in specific brain networks coupled with poor neuropsychological functioning and heightened impulsivity may characterize individuals with AUD, who can be efficiently identified using classification algorithms such as Random Forest.
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http://dx.doi.org/10.3390/brainsci10020115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071377PMC
February 2020

Differential Patterns of Visual Sensory Alteration Underlying Face Emotion Recognition Impairment and Motion Perception Deficits in Schizophrenia and Autism Spectrum Disorder.

Biol Psychiatry 2019 10 29;86(7):557-567. Epub 2019 May 29.

Schizophrenia Research Division, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York; Department of Psychiatry, Columbia University Medical Center, New York, New York.

Background: Impaired face emotion recognition (FER) and abnormal motion processing are core features in schizophrenia (SZ) and autism spectrum disorder (ASD) that have been linked to atypical activity within the visual cortex. Despite overlaps, only a few studies have directly explored convergent versus divergent neural mechanisms of altered visual processing in ASD and SZ. We employed a multimodal imaging approach to evaluate FER and motion perception in relation to functioning of subcortical and cortical visual regions.

Methods: Subjects were 20 high-functioning adults with ASD, 19 patients with SZ, and 17 control participants. Behavioral measures of coherent motion sensitivity and FER along with electrophysiological and functional magnetic resonance imaging measures of visual pattern and motion processing were obtained. Resting-state functional magnetic resonance imaging was used to assess the relationship between corticocortical and thalamocortical connectivity and atypical visual processing.

Results: SZ and ASD participants had intercorrelated deficits in FER and motion sensitivity. In both groups, reduced motion sensitivity was associated with reduced functional magnetic resonance imaging activation in the occipitotemporal cortex and lower delta-band electroencephalogram power. In ASD, FER deficits correlated with hyperactivation of dorsal stream regions and increased evoked theta power. Activation of the pulvinar correlated with abnormal alpha-band modulation in SZ and ASD with under- and overmodulation, respectively, predicting increased clinical symptoms in both groups.

Conclusions: SZ and ASD participants showed equivalent deficits in FER and motion sensitivity but markedly different profiles of physiological dysfunction. The specific pattern of deficits observed in each group may help guide development of treatments designed to downregulate versus upregulate visual processing within the respective clinical groups.
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http://dx.doi.org/10.1016/j.biopsych.2019.05.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197738PMC
October 2019

Hippocampal volumetric integrity in mesial temporal lobe epilepsy: A fast novel method for analysis of structural MRI.

Epilepsy Res 2019 08 24;154:157-162. Epub 2019 May 24.

Comprehensive Epilepsy Center, Department of Neurology, NYU Langone Health, United States. Electronic address:

Objective: We investigate whether a rapid and novel automated MRI processing technique for assessing hippocampal volumetric integrity (HVI) can be used to identify hippocampal sclerosis (HS) in patients with mesial temporal lobe epilepsy (mTLE) and determine its performance relative to hippocampal volumetry (HV) and visual inspection.

Methods: We applied the HVI technique to T1-weighted brain images from healthy control (n = 35), mTLE (n = 29), non-HS temporal lobe epilepsy (TLE, n = 44), and extratemporal focal epilepsy (EXTLE, n = 25) subjects imaged using a standardized epilepsy research imaging protocol and on non-standardized clinically acquired images from mTLE subjects (n = 40) to investigate if the technique is translatable to clinical practice. Performance of HVI, HV, and visual inspection was assessed using receiver operating characteristic (ROC) analysis.

Results: mTLE patients from both research and clinical groups had significantly reduced ipsilateral HVI relative to controls (effect size: -0.053, 5.62%, p =  0.002 using a standardized research imaging protocol). For lateralizing mTLE, HVI had a sensitivity of 88% compared with a HV sensitivity of 92% when using specificity equal to 70%.

Conclusions: The novel HVI approach can effectively detect HS in clinical populations, with an average image processing time of less than a minute. The fast processing speed suggests this technique could have utility as a quantitative tool to assist with imaging-based diagnosis and lateralization of HS in a clinical setting.
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http://dx.doi.org/10.1016/j.eplepsyres.2019.05.014DOI Listing
August 2019

Citalopram in first episode schizophrenia: The DECIFER trial.

Schizophr Res 2019 06 30;208:331-337. Epub 2019 Jan 30.

National Clinical Research Center for Mental Disorders, Mental Health Institute, The Second Xiangya Hospital of Central South University, 139 Renmin Middle Road, Changsha, Hunan, China.

Antidepressants are frequently prescribed in first episode schizophrenia (FES) patients for negative symptoms or for subsyndromal depressive symptoms, but therapeutic benefit has not been established, despite evidence of efficacy in later-stage schizophrenia. We conducted a 52 week, placebo-controlled add-on trial of citalopram in patients with FES who did not meet criteria for major depression to determine whether maintenance therapy with citalopram would improve outcomes by preventing or improving negative and depressive symptoms. Primary outcomes were negative symptoms measured by the Scale for Assessment of Negative Symptoms and depressive symptoms measured by the Calgary Depression Scale for Schizophrenia; both were analyzed by an intent-to-treat, mixed effects, area-under-the-curve analysis to assess the cumulative effects of symptom improvement and symptom prevention over a one-year period. Ninety-five patients were randomized and 52 (54%) completed the trial. Negative symptoms were reduced with citalopram compared to placebo (p = .04); the effect size of citalopram versus placebo was 0.32 for participants with a duration of untreated psychosis (DUP) of <18 weeks (median split) and 0.52 with a DUP >18 weeks. Rates of new-onset depression did not differ between groups; improvement in depressive symptoms was greater with placebo than citalopram (p = .02). Sexual side effects were more common with citalopram, but overall treatment-emergent side effects were not increased compared to placebo. In conclusion, citalopram may reduce levels of negative symptoms, particularly in patients with longer DUP, but we found no evidence of benefit for subsyndromal depressive symptoms.
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http://dx.doi.org/10.1016/j.schres.2019.01.028DOI Listing
June 2019

Association of Hippocampal Atrophy With Duration of Untreated Psychosis and Molecular Biomarkers During Initial Antipsychotic Treatment of First-Episode Psychosis.

JAMA Psychiatry 2018 04;75(4):370-378

Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China.

Importance: Duration of untreated psychosis (DUP) has been associated with poor outcomes in schizophrenia, but the mechanism responsible for this association is not known.

Objectives: To determine whether hippocampal volume loss occurs during the initial 8 weeks of antipsychotic treatment and whether it is associated with DUP, and to examine molecular biomarkers in association with hippocampal volume loss and DUP.

Design, Setting, And Participants: A naturalistic longitudinal study with matched healthy controls was conducted at Shanghai Mental Health Center. Between March 5, 2013, and October 8, 2014, 71 medication-naive individuals with nonaffective first-episode psychosis (FEP) and 73 age- and sex-matched healthy controls were recruited. After approximately 8 weeks, 31 participants with FEP and 32 controls were reassessed.

Exposures: The participants with FEP were treated according to standard clinical practice with second-generation antipsychotics.

Main Outcomes And Measures: Hippocampal volumetric integrity (HVI) (an automated estimate of the parenchymal fraction in a standardized hippocampal volume of interest), DUP, 13 peripheral molecular biomarkers, and 14 single-nucleotide polymorphisms from 12 candidate genes were determined.

Results: The full sample consisted of 71 individuals with FEP (39 women and 32 men; mean [SD] age, 25.2 [7.7] years) and 73 healthy controls (40 women and 33 men; mean [SD] age, 23.9 [6.4] years). Baseline median left HVI was lower in the FEP group (n = 57) compared with the controls (n = 54) (0.9275 vs 0.9512; difference in point estimate, -0.020 [95% CI, -0.029 to -0.010]; P = .001). During approximately 8 weeks of antipsychotic treatment, left HVI decreased in 24 participants with FEP at a median annualized rate of -.03791 (-4.1% annualized change from baseline) compared with an increase of 0.00115 (0.13% annualized change from baseline) in 31 controls (difference in point estimate, -0.0424 [95% CI, -0.0707 to -0.0164]; P = .001). The change in left HVI was inversely associated with DUP (r = -0.61; P = .002). Similar results were found for right HVI, although the association between change in right HVI and DUP did not achieve statistical significance (r = -0.35; P = .10). Exploratory analyses restricted to the left HVI revealed an association between left HVI and markers of inflammation, oxidative stress, brain-derived neurotrophic factor, glial injury, and markers reflecting dopaminergic and glutamatergic transmission.

Conclusions And Relevance: An association between longer DUP and accelerated hippocampal atrophy during initial treatment suggests that psychosis may have persistent, possibly deleterious, effects on brain structure. Additional studies are needed to replicate these exploratory findings of molecular mechanisms by which untreated psychosis may affect hippocampal volume and to determine whether these effects account for the known association between longer DUP and poor outcome.
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http://dx.doi.org/10.1001/jamapsychiatry.2017.4595DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5875378PMC
April 2018

A six-month longitudinal evaluation significantly improves accuracy of predicting incipient Alzheimer's disease in mild cognitive impairment.

J Neuroradiol 2017 Oct 2;44(6):381-387. Epub 2017 Jul 2.

The Nathan S. Kline institute for psychiatric research, 140, Old Orangeburg road, 10962 Orangeburg, New York, USA; Department of psychiatry, New York university school of medicine, New York, USA. Electronic address:

Rationale And Objectives: Early prediction of incipient Alzheimer's disease (AD) dementia in individuals with mild cognitive impairment (MCI) is important for timely therapeutic intervention and identifying participants for clinical trials at greater risk of developing AD. Methods to predict incipient AD in MCI have mostly utilized cross-sectional data. Longitudinal data enables estimation of the rate of change of variables, which along with the variable levels have been shown to improve prediction power. While some efforts have already been made in this direction, all previous longitudinal studies have been based on observation periods longer than one year, hence limiting their practical utility. It remains to be seen if follow-up evaluations within shorter intervals can significantly improve the accuracy of prediction in this problem. Our aim was to determine the added value of incorporating 6-month longitudinal data for predicting progression from MCI to AD.

Materials And Methods: Using 6-months longitudinal data from 247 participants with MCI, we trained two Random Forest classifiers to distinguish between progressive MCI (n=162) and stable MCI (n=85) cases. These models utilized structural MRI, neurocognitive assessments, and demographic information. The first model (cross-sectional) only used baseline data. The second model (longitudinal) used data from both baseline and a 6-month follow-up evaluation allowing the model to additionally incorporate biomarkers' rate of change.

Results: The longitudinal model (AUC=0.87; accuracy=80.2%) performed significantly better (P<0.05) than the cross-sectional model (AUC=0.82; accuracy=71.7%).

Conclusion: Short-term longitudinal assessments significantly enhance the performance of AD prediction models.
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http://dx.doi.org/10.1016/j.neurad.2017.05.008DOI Listing
October 2017

Prediction of Incipient Alzheimer's Disease Dementia in Patients with Mild Cognitive Impairment.

J Alzheimers Dis 2017 ;55(1):269-281

The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.

Background: Mild cognitive impairment (MCI) is a transitional stage from normal aging to Alzheimer's disease (AD) dementia. It is extremely important to develop criteria that can be used to separate the MCI subjects at imminent risk of conversion to Alzheimer-type dementia from those who would remain stable. We have developed an automatic algorithm for computing a novel measure of hippocampal volumetric integrity (HVI) from structural MRI scans that may be useful for this purpose.

Objective: To determine the utility of HVI in classification between stable and progressive MCI patients using the Random Forest classification algorithm.

Methods: We used a 16-dimensional feature space including bilateral HVI obtained from baseline and one-year follow-up structural MRI, cognitive tests, and genetic and demographic information to train a Random Forest classifier in a sample of 164 MCI subjects categorized into two groups [progressive (n = 86) or stable (n = 78)] based on future conversion (or lack thereof) of their diagnosis to probable AD.

Results: The overall accuracy of classification was estimated to be 82.3% (86.0% sensitivity, 78.2% specificity). The accuracy in women (89.1%) was considerably higher than that in men (78.9%). The prediction accuracy achieved in women is the highest reported in any previous application of machine learning to AD diagnosis in MCI.

Conclusion: The method presented in this paper can be used to separate stable MCI patients from those who are at early stages of AD dementia with high accuracy. There may be stronger indicators of imminent AD dementia in women with MCI as compared to men.
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http://dx.doi.org/10.3233/JAD-160594DOI Listing
February 2018

Hippocampal volume and integrity as predictors of cognitive decline in intact elderly.

Neuroreport 2016 08;27(11):869-73

aDepartment of Psychology, Liverpool Hope University bSchool of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, UK cDivision of Biostatistics, New York State Psychiatric Institute and Columbia University, New York dNathan Kline Institute for Psychiatric Research, Orangeburg eDepartments of Child and Adolescent Psychiatry and Population Health, School of Medicine fDepartment of Psychiatry, New York University, New York City, New York, USA gGerman Center for Neurodegenerative Diseases (DZNE) hDepartment of Psychosomatic Medicine, University of Rostock, Rostock, Germany.

The risk of Alzheimer's disease can be predicted by volumetric analyses of MRI data in the medial temporal lobe. The present study compared a volumetric measurement of the hippocampus with a novel measure of hippocampal integrity (HI) derived from the ratio of parenchyma volume over total volume. Participants were cognitively intact and aged 60 years or older at baseline, and were tested twice, roughly 3 years apart. Participants had been recruited for a study on late-life major depression (LLMD) and were evenly split between depressed patients and controls. Linear regression models were applied to the data with a cognitive composite score as the outcome, and HI and volume, together or separately, as predictors. Subsequent cognitive performance was predicted well by models that included an interaction between HI and LLMD status, such that lower HI scores predicted more cognitive decline in depressed patients. More research is needed, but tentative results from this study appear to suggest that the newly introduced measure HI is an effective tool for the purpose of predicting future changes in general cognitive ability, and especially so in individuals with LLMD.
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http://dx.doi.org/10.1097/WNR.0000000000000629DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4929020PMC
August 2016

Predicting progression from mild cognitive impairment to Alzheimer's disease using longitudinal callosal atrophy.

Alzheimers Dement (Amst) 2016 8;2:68-74. Epub 2016 Mar 8.

Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry, New York University School of Medicine, New York, NY, USA.

Introduction: We investigate whether longitudinal callosal atrophy could predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD).

Methods: Longitudinal (baseline + 1-year follow-up) MRI scans of 132 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were used. A total of 54 subjects did not convert to AD over an average (±SD) follow-up of 5.46 (±1.63) years, whereas 78 converted to AD with an average conversion time of 2.56 (±1.65) years. Annual change in the corpus callosum thickness profile was calculated from the baseline and 1-year follow-up MRI. A logistic regression model with fused lasso regularization for prediction was applied to the annual changes.

Results: We found a sex difference. The accuracy of prediction was 84% in females and 61% in males. The discriminating regions of corpus callosum differed between sexes. In females, the genu, rostrum, and posterior body had predictive power, whereas the genu and splenium were relevant in males.

Discussion: Annual callosal atrophy predicts MCI-to-AD conversion in females more accurately than in males.
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http://dx.doi.org/10.1016/j.dadm.2016.01.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4879655PMC
May 2016

Abnormal white matter microstructure in drug-naive first episode schizophrenia patients before and after eight weeks of antipsychotic treatment.

Schizophr Res 2016 Apr 3;172(1-3):1-8. Epub 2016 Feb 3.

Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, PR China; Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiaotong University, Shanghai, 200030, PR China. Electronic address:

Background: Abnormal white matter integrity has been reported among first episode schizophrenia patients. However, findings on whether it can be reversed by short-term antipsychotic medications are inconsistent.

Method: Diffusion tensor imaging (DTI) was obtained from 55 drug-naive first episode schizophrenia patients and 61 healthy controls, and was repeated among 25 patients and 31 controls after 8 weeks during which patients were medicated with antipsychotics. White matter integrity is measured using fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). These measures showing a group difference by Tract-based spatial statistics (TBSS) at baseline were extracted for longitudinal comparisons.

Results: At baseline, patients exhibited lower FA, higher MD and higher RD versus controls in forceps, left superior longitudinal fasciculus, inferior fronto-occipital fasciculus, left corticospinal tract, left uncinate fasciculus, left anterior thalamic radiation, and bilateral inferior longitudinal fasciculi. FA values of schizophrenia patients correlated with their negative symptoms (r=-0.412, P=0.002), working memory (r=0.377, P=0.005) and visual learning (r=0.281, P=0.038). The longitudinal changes in DTI indices in these tracts did not differ between patients and controls. However, among the patients the longitudinal changes in FA values in left superior longitudinal fasciculus correlated with the change of positive symptoms (r=-0.560, p=0.004), and the change of processing speed (r=0.469, p=0.018).

Conclusions: White matter deficits were validated in the present study by a relatively large sample of medication naïve and first episode schizophrenia patients. They could be associated with negative symptoms and cognitive impairment, whereas improvement in white matter integrity of left superior longitudinal fasciculus correlated with improvement in psychosis and processing speed. Further examination of treatment-related changes in white matter integrity may provide clues to the mechanism of antipsychotic response and provide a biomarker for clinical studies.
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http://dx.doi.org/10.1016/j.schres.2016.01.051DOI Listing
April 2016

Analysis of the MIRIAD Data Shows Sex Differences in Hippocampal Atrophy Progression.

J Alzheimers Dis 2016 ;50(3):847-57

The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.

Background: Hippocampus (HC) atrophy is a hallmark of early Alzheimer's disease (AD). Atrophy rates can be measured by high-resolution structural MRI. Longitudinal studies have previously shown sex differences in the progression of functional and cognitive deficits and rates of brain atrophy in early AD dementia. It is important to corroborate these findings on independent datasets.

Objective: To study temporal rates of HC atrophy over a one-year period in probable AD patients and cognitively normal (CN) subjects by longitudinal MRI scans obtained from the Minimal Interval Resonance Imaging in AD (MIRIAD) database.

Methods: We used a novel algorithm to compute an index of hippocampal (volumetric) integrity (HI) at baseline and one-year follow-up in 43 mild-moderate probable AD patients and 22 CN subjects in MIRIAD. The diagnostic power of longitudinal HI measurement was assessed using a support vector machines (SVM) classifier.

Results: The HI was significantly reduced in the AD group (p <  10(-20)). In addition, the annualized percentage rate of reduction in HI was significantly greater in the AD group (p <  10(-13)). Within the AD group, the annual reduction of HI in women was significantly greater than in men (p = 0.008). The accuracy of SVM classification between AD and CN subjects was estimated to be 97% by 10-fold cross-validation.

Conclusion: In the MIRIAD patients with probable AD, the HC atrophies at a significantly faster rate in women as compared to men. Female sex is a risk factor for faster descent into AD. The HI measure has potential for AD diagnosis, as a biomarker of AD progression and a therapeutic target in clinical trials.
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http://dx.doi.org/10.3233/JAD-150780DOI Listing
October 2016

The corpus callosum and recovery of working memory after epilepsy surgery.

Epilepsia 2015 Apr 16;56(4):527-34. Epub 2015 Feb 16.

Department of Neurology, Comprehensive Epilepsy Center, New York University School of Medicine, New York, New York, U.S.A.

Objective: For patients with medically intractable focal epilepsy, the benefit of epilepsy surgery must be weighed against the risk of cognitive decline. Clinical factors such as age and presurgical cognitive level partially predict cognitive outcome; yet, little is known about the role of cross-hemispheric white matter pathways in supporting postsurgical recovery of cognitive function. The purpose of this study is to determine whether the presurgical corpus callosum (CC) midsagittal area is associated with pre- to postsurgical change following epilepsy surgery.

Methods: In this observational study, we retrospectively identified 24 adult patients from an epilepsy resection series who completed preoperative high-resolution T1 -weighted magnetic resonance imaging (MRI) scans, as well as pre- and postsurgical neuropsychological testing. The total area and seven subregional areas of the CC were measured on the midsagittal MRI slice using an automated method. Standardized indices of auditory-verbal working memory and delayed memory were used to probe cognitive change from pre- to postsurgery. CC total and subregional areas were regressed on memory-change scores, after controlling for overall brain volume, age, presurgical memory scores, and duration of epilepsy.

Results: Patients had significantly reduced CC area relative to healthy controls. We found a positive relationship between CC area and change in working memory, but not delayed memory; specifically, the larger the CC, the greater the postsurgical improvement in working memory (β = 0.523; p = 0.009). Effects were strongest in posterior CC subregions. There was no relationship between CC area and presurgical memory scores.

Significance: Findings indicate that larger CC area, measured presurgically, is related to improvement in working memory abilities following epilepsy surgery. This suggests that transcallosal pathways may play an important, yet little understood, role in postsurgical recovery of cognitive functions.
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http://dx.doi.org/10.1111/epi.12931DOI Listing
April 2015

Corpus callosum atrophy rate in mild cognitive impairment and prodromal Alzheimer's disease.

J Alzheimers Dis 2015 ;45(3):921-31

The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA Department of Psychiatry, New York University School of Medicine, New York, NY, USA.

Background: Corpus callosum (CC) size and shape have been previously studied in Alzheimer's disease (AD) with the majority of studies having been cross-sectional. Due to the large variance in normal CC morphology, cross-sectional studies are limited in statistical power. Determining individual rates of change requires longitudinal data. Physiological changes are particularly relevant in mild cognitive impairment (MCI), in which CC morphology has not been previously studied longitudinally.

Objective: To study temporal rates of change in CC morphology in MCI patients over a one-year period, and to determine whether these rates differ between MCI subjects who converted to AD (MCI-C) and those who did not (MCI-NC) over an average (±SD) observation period of 5.4 (±1.6) years.

Methods: We used a novel multi-atlas based algorithm to segment the mid-sagittal cross-sectional area of the CC in longitudinal MRI scans. Rates of change of CC circularity, total area, and five sub-areas were compared between 57 MCI-NC and 81 MCI-C subjects.

Results: The CC became less circular (-0.89% per year in MCI-NC, -1.85% per year in MCI-C) with time, with faster decline in MCI-C (p = 0.0002). In females, atrophy rates were higher in MCI-C relative to MCI-NC in total CC area (p = 0.0006), genu/rostrum (p = 0.005), and splenium (0.002). In males, these rates did not differ between groups.

Conclusion: A greater than normal decline in CC circularity was shown to be an indicator of prodromal AD in MCI subjects. This measure is potentially useful as an imaging biomarker of disease and a therapeutic target in clinical trials.
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http://dx.doi.org/10.3233/JAD-142631DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4451933PMC
January 2016

Corpus callosum shape and size changes in early Alzheimer's disease: a longitudinal MRI study using the OASIS brain database.

J Alzheimers Dis 2014 ;39(1):71-8

The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA Department of Psychiatry, New York University School of Medicine, New York, NY, USA.

Background: Alzheimer's disease (AD) has been shown to be associated with shrinkage of the corpus callosum mid-sagittal cross-sectional area (CCA).

Objective: To study temporal rates of corpus callosum atrophy not previously reported for early AD.

Methods: We used longitudinal MRI scans to study the rates of change of CCA and circularity (CIR), a measure of its shape, in normal controls (NC, n = 75), patients with very mild AD (AD-VM, n = 51), and mild AD (AD-M, n = 21).

Results: There were significant reduction rates in CCA and CIR in all three groups. While CCA reduction rates were not statistically different between groups, the CIR declined faster in AD-VM (p < 0.03) and AD-M (p < 0.0001) relative to NC, and in AD-M relative to AD-VM (p < 0.0004).

Conclusion: CIR declines at an accelerated rate with AD severity. Its rate of change is more closely associated with AD progression than CCA or any of its sub-regions. CIR may be a useful group biomarker for objective assessment of treatments that aim to slow AD progression.
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http://dx.doi.org/10.3233/JAD-131526DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4314946PMC
September 2014

Application of fused lasso logistic regression to the study of corpus callosum thickness in early Alzheimer's disease.

J Neurosci Methods 2014 Jan 9;221:78-84. Epub 2013 Oct 9.

The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA. Electronic address:

We propose a fused lasso logistic regression to analyze callosal thickness profiles. The fused lasso regression imposes penalties on both the l1-norm of the model coefficients and their successive differences, and finds only a small number of non-zero coefficients which are locally constant. An iterative method of solving logistic regression with fused lasso regularization is proposed to make this a practical procedure. In this study we analyzed callosal thickness profiles sampled at 100 equal intervals between the rostrum and the splenium. The method was applied to corpora callosa of elderly normal controls (NCs) and patients with very mild or mild Alzheimer's disease (AD) from the Open Access Series of Imaging Studies (OASIS) database. We found specific locations in the genu and splenium of AD patients that are proportionally thinner than those of NCs. Callosal thickness in these regions combined with the Mini Mental State Examination scores differentiated AD from NC with 84% accuracy.
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http://dx.doi.org/10.1016/j.jneumeth.2013.09.017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4314964PMC
January 2014

Overlapping and distinct gray and white matter abnormalities in schizophrenia and bipolar I disorder.

Bipolar Disord 2013 Sep 25;15(6):680-93. Epub 2013 Jun 25.

The Feinstein Institute for Medical Research, North Shore-LIJ Health System, Manhasset.

Objectives: Schizophrenia and bipolar disorder may share common neurobiological mechanisms, but few studies have directly compared gray and white matter structure in these disorders. We used diffusion-weighted magnetic resonance imaging and a region of interest based analysis to identify overlapping and distinct gray and white matter abnormalities in 35 patients with schizophrenia and 20 patients with bipolar I disorder in comparison to 56 healthy volunteers.

Methods: We examined fractional anisotropy within the white matter and mean diffusivity within the gray matter in 42 regions of interest defined on a probabilistic atlas following non-linear registration of the images to atlas space.

Results: Patients with schizophrenia had significantly lower fractional anisotropy in temporal (superior temporal and parahippocampal) and occipital (superior and middle occipital) white matter compared to patients with bipolar disorder and healthy volunteers. By contrast, both patient groups demonstrated significantly higher mean diffusivity in frontal (inferior frontal and lateral orbitofrontal) and temporal (superior temporal and parahippocampal) gray matter compared to healthy volunteers, but did not differ from each other.

Conclusions: Our study implicates overlapping gray matter frontal and temporal lobe structural alterations in the neurobiology of schizophrenia and bipolar I disorder, but suggests that temporal and occipital lobe white matter deficits may be an additional risk factor for schizophrenia. Our findings may have relevance for future diagnostic classification systems and the identification of susceptibility genes for these disorders.
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http://dx.doi.org/10.1111/bdi.12096DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3762889PMC
September 2013

Corpus callosum shape changes in early Alzheimer's disease: an MRI study using the OASIS brain database.

Brain Struct Funct 2014 Jan 16;219(1):343-52. Epub 2013 Jan 16.

The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY, 10962, USA,

The corpus callosum (CC) is the largest fiber bundle connecting the left and right cerebral hemispheres. It has been a region examined extensively for indications of various pathologies, including Alzheimer's disease (AD). Almost all previous studies of the CC in AD have been concerned with its size, particularly its mid-sagittal cross-sectional area (CCA). In this study, we show that the CC shape, characterized by its circularity (CIR), may be affected more profoundly than its size in early AD. MRI scans (n = 196) were obtained from the publicly available Open Access Series of Imaging Studies database. The CC cross-sectional region on the mid-sagittal section of the brain was automatically segmented using a novel algorithm. The CCA and CIR were compared in 98 normal controls (NC) subjects, 70 patients with very mild AD (AD-VM), and 28 patients with mild AD (AD-M). Statistical analysis of covariance controlling for age and intracranial capacity showed that both the CIR and the CCA were significantly reduced in the AD-VM group relative to the NC group (CIR: p = 0.004; CCA: p = 0.005). However, only the CIR was significantly different between the AD-M and AD-VM groups (p = 0.006) being smaller in the former. The CCA was not significantly different between the AD-M and AD-VM groups. The results suggest that CC shape may be a more sensitive marker than its size for monitoring the progression of AD. In order to facilitate independent analyses, the CC segmentations and the CCA and CIR data used in this study have been made publicly available (http://www.nitrc.org/projects/art).
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http://dx.doi.org/10.1007/s00429-013-0503-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657596PMC
January 2014

Sexual dimorphism in the human corpus callosum: an MRI study using the OASIS brain database.

Cereb Cortex 2013 Oct 13;23(10):2514-20. Epub 2012 Aug 13.

Center for Advanced Brain Imaging, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA.

A number of studies have reported that, "relative to brain size," the midsagittal corpus callosum cross-sectional area (CCA) in females is on average larger than in males. However, others suggest that these may be spurious differences created in the CCA-to-brain-size ratio because brain size tends to be larger in males. To help resolve this controversy, we measured the CCA on all 316 magnetic resonance imaging (MRI) scans of normal subjects (18-94 years) in the OASIS (Open Access Series of Imaging Studies) cross-sectional dataset, and used multiple regression analysis to statistically control for the confounding effects of brain size and age to test the null hypothesis that the average CCA is not different between genders. An additional analysis was performed on a subset of 74 young adults (37 males and 37 females; 18-29 years) matched closely to brain size. Our null hypothesis was rejected in both analyses. In the entire sample (n= 316), controlling for brain size and age, the average CCA was significantly (P< 0.03) larger in females. The difference favoring females was more pronounced in the young adults cohort (P< 0.0005). These results provide strong additional evidence that the CCA is larger in females after correcting for the confounding effect of brain size.
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http://dx.doi.org/10.1093/cercor/bhs253DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767965PMC
October 2013

Sexual dimorphism in the human corpus callosum: an MRI study using the OASIS brain database.

Cereb Cortex 2013 Oct 13;23(10):2514-20. Epub 2012 Aug 13.

Center for Advanced Brain Imaging, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA.

A number of studies have reported that, "relative to brain size," the midsagittal corpus callosum cross-sectional area (CCA) in females is on average larger than in males. However, others suggest that these may be spurious differences created in the CCA-to-brain-size ratio because brain size tends to be larger in males. To help resolve this controversy, we measured the CCA on all 316 magnetic resonance imaging (MRI) scans of normal subjects (18-94 years) in the OASIS (Open Access Series of Imaging Studies) cross-sectional dataset, and used multiple regression analysis to statistically control for the confounding effects of brain size and age to test the null hypothesis that the average CCA is not different between genders. An additional analysis was performed on a subset of 74 young adults (37 males and 37 females; 18-29 years) matched closely to brain size. Our null hypothesis was rejected in both analyses. In the entire sample (n= 316), controlling for brain size and age, the average CCA was significantly (P< 0.03) larger in females. The difference favoring females was more pronounced in the young adults cohort (P< 0.0005). These results provide strong additional evidence that the CCA is larger in females after correcting for the confounding effect of brain size.
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http://dx.doi.org/10.1093/cercor/bhs253DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767965PMC
October 2013

White matter integrity and lack of insight in schizophrenia and schizoaffective disorder.

Schizophr Res 2011 May 22;128(1-3):76-82. Epub 2011 Mar 22.

Department of Psychiatry, New York University School of Medicine, New York, NY 10016, USA.

Objective: Poor insight into illness is commonly associated with schizophrenia and has implications for the clinical outcome of the disease. A better understanding of the neurobiology of these insight deficits may help the development of new treatments targeting insight. Despite the importance of this issue, the neural correlates of insight deficits in schizophrenia remain poorly understood.

Method: Thirty-six individuals diagnosed with schizophrenia or schizoaffective disorder underwent diffusion tensor imaging (DTI). The subjects were assessed on two dimensions of insight (symptom awareness and attribution of symptoms) using the Scale to Assess Unawareness of Mental Disorder (SUMD). Level of psychosis was assessed with the Positive and Negative Syndrome Scale (PANSS).

Results: White matter abnormalities in the right superior frontal gyrus, left middle frontal gyrus, bilateral parahippocampal gyrus, adjacent to the right caudate head, right thalamus, left insula, left lentiform nucleus, left fusiform gyrus, bilateral posterior cingulate, left anterior cingulate, right cingulate gyrus, left lingual gyrus, and bilateral claustrum were associated with symptom unawareness. Misattribution of symptoms was related to deficits in the white matter adjacent to the right lentiform nucleus, left middle temporal gyrus, and the right precuneus.

Conclusions: Impaired insight in schizophrenia implicates a complex neural circuitry: white matter deficits in fronto-temporo brain regions are linked to symptom unawareness; compromised temporal and parietal white matter regions are involved in the misattribution of symptoms. These findings suggest the multidimensional construct of insight has multiple neural determinants.
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http://dx.doi.org/10.1016/j.schres.2011.02.020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3085627PMC
May 2011

Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging.

Magn Reson Med 2011 Mar 28;65(3):823-36. Epub 2010 Oct 28.

Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York 10016, USA.

This article presents two related advancements to the diffusional kurtosis imaging estimation framework to increase its robustness to noise, motion, and imaging artifacts. The first advancement substantially improves the estimation of diffusion and kurtosis tensors parameterizing the diffusional kurtosis imaging model. Rather than utilizing conventional unconstrained least squares methods, the tensor estimation problem is formulated as linearly constrained linear least squares, where the constraints ensure physically and/or biologically plausible tensor estimates. The exact solution to the constrained problem is found via convex quadratic programming methods or, alternatively, an approximate solution is determined through a fast heuristic algorithm. The computationally more demanding quadratic programming-based method is more flexible, allowing for an arbitrary number of diffusion weightings and different gradient sets for each diffusion weighting. The heuristic algorithm is suitable for real-time settings such as on clinical scanners, where run time is crucial. The advantage offered by the proposed constrained algorithms is demonstrated using in vivo human brain images. The proposed constrained methods allow for shorter scan times and/or higher spatial resolution for a given fidelity of the diffusional kurtosis imaging parametric maps. The second advancement increases the efficiency and accuracy of the estimation of mean and radial kurtoses by applying exact closed-form formulae.
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http://dx.doi.org/10.1002/mrm.22655DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3042509PMC
March 2011

Diffusion tensor imaging reliably differentiates patients with schizophrenia from healthy volunteers.

Hum Brain Mapp 2011 Jan;32(1):1-9

Center for Advanced Brain Imaging, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962, USA.

The objective of this research was to determine whether fractional anisotropy (FA) and mean diffusivity (MD) maps derived from diffusion tensor imaging (DTI) of the brain are able to reliably differentiate patients with schizophrenia from healthy volunteers. DTI and high resolution structural magnetic resonance scans were acquired in 50 patients with schizophrenia and 50 age- and sex-matched healthy volunteers. FA and MD maps were estimated from the DTI data and spatially normalized to the Montreal Neurologic Institute standard stereotactic space. Individuals were divided randomly into two groups of 50, a training set, and a test set, each comprising 25 patients and 25 healthy volunteers. A pattern classifier was designed using Fisher's linear discriminant analysis (LDA) based on the training set of images to categorize individuals in the test set as either patients or healthy volunteers. Using the FA maps, the classifier correctly identified 94% of the cases in the test set (96% sensitivity and 92% specificity). The classifier achieved 98% accuracy (96% sensitivity and 100% specificity) when using the MD maps as inputs to distinguish schizophrenia patients from healthy volunteers in the test dataset. Utilizing FA and MD data in combination did not significantly alter the accuracy (96% sensitivity and specificity). Patterns of water self-diffusion in the brain as estimated by DTI can be used in conjunction with automated pattern recognition algorithms to reliably distinguish between patients with schizophrenia and normal control subjects.
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http://dx.doi.org/10.1002/hbm.20995DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896986PMC
January 2011

Diffusion-tensor imaging implicates prefrontal axonal injury in executive function impairment following very mild traumatic brain injury.

Radiology 2009 Sep 30;252(3):816-24. Epub 2009 Jun 30.

Gruss Magnetic Resonance Research Center, Albert Einstein College of Medicine, 1300 Morris Park Ave, Bronx, NY 10461, USA.

Purpose: To determine whether frontal white matter diffusion abnormalities can help predict acute executive function impairment after mild traumatic brain injury (mTBI).

Materials And Methods: This study had institutional review board approval, included written informed consent, and complied with HIPAA. Diffusion-tensor imaging and standardized neuropsychologic assessments were performed in 20 patients with mTBI within 2 weeks of injury and 20 matched control subjects. Fractional anisotropy (FA) and mean diffusivity (MD) images (imaging parameters: 3.0 T, 25 directions, b = 1000 sec/mm(2)) were compared by using whole-brain voxelwise analysis. Spearman correlation analyses were performed to evaluate associations between diffusion measures and executive function.

Results: Multiple clusters of lower frontal white matter FA, including the dorsolateral prefrontal cortex (DLPFC), were present in patients (P < .005), with several clusters also demonstrating higher MD (P < .005). Patients performed worse on tests of executive function. Lower DLPFC FA was significantly correlated with worse executive function performance in patients (P < .05).

Conclusion: Impaired executive function following mTBI is associated with axonal injury involving the DLPFC.
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http://dx.doi.org/10.1148/radiol.2523081584DOI Listing
September 2009

Model-based automatic detection of the anterior and posterior commissures on MRI scans.

Neuroimage 2009 Jul 3;46(3):677-82. Epub 2009 Mar 3.

Center for Advanced Brain Imaging, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA.

The projections of the anterior and posterior commissures (AC/PC) on the mid-sagittal plane of the human brain are important landmarks in neuroimaging. They can be used, for example, during MRI scanning for acquiring the imaging sections in a standard orientation. In post-acquisition image processing, these landmarks serve to establish an anatomically-based frame of reference within the brain that can be extremely useful in designing automated image analysis algorithms such as image segmentation and registration methods. This paper presents a fully automatic model-based algorithm for AC/PC detection on MRI scans. The algorithm utilizes information from a number of model images on which the locations of the AC/PC and a reference point (the vertex of the superior pontine sulcus) are known. This information is then used to locate the landmarks on test scans by template matching. The algorithm is designed to be fast, robust, and accurate. The method is flexible in that it can be trained to work on different image contrasts, optimized for different populations, or scanning modes. To assess the effectiveness of this technique, we compared automatically and manually detected landmark locations on 84 T(1)-weighted and 42 T(2)-weighted test scans. Overall, the average Euclidean distance between automatically and manually detected landmarks was 1.1 mm. A software implementation of the algorithm is freely available online at www.nitrc.org/projects/art.
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http://dx.doi.org/10.1016/j.neuroimage.2009.02.030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2674131PMC
July 2009

Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

Neuroimage 2009 Jul 13;46(3):786-802. Epub 2009 Jan 13.

New York State Psychiatric Institute, Columbia University, NY, NY 10032, USA.

All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.
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http://dx.doi.org/10.1016/j.neuroimage.2008.12.037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2747506PMC
July 2009