Intranasal Oxytocin Increases Head Motion During Functional MRI Scanning.

Oxytocin is a neuropeptide associated with prosocial behaviors, such as parent-child bonding, eye contact, and sexual activity. Intranasally-administered oxytocin has been widely used to study its effects on the brain using functional magnetic resonance imaging. Head motion is a significant confounding variable which was assessed as part of a double blind, placebo-controlled crossover study. Twenty-four mothers with drug addiction problems were initially recruited, along with 22 healthy control mothers, to test whether intranasal oxytocin enhances functional brain responses to images of their own versus unknown infant faces. Significant differences in head motion between oxytocin/placebo conditions and addiction/control groups were discovered. Administration of intranasal oxytocin was associated with more frequent counts of head motion exceeding 3 mm of framewise displacement, independent of group status (z=2.89, p=0.004). This effect was seen more strongly in the control group (z=2.30, p=0.02) than the addiction group (z=1.77, p=0.08). The addiction group was more likely to show increased head motion, independent of oxytocin or placebo condition (z=2.21, p=0.03). When examining the mean head motion across all time points, as opposed to the count of large movements, oxytocin's effect was limited to the addiction group (z=2.58, p=0.01), with a significant group by condition interaction effect observed. Intranasally-administered oxytocin may therefore have a confounding effect on functional MRI scanning results via its independent effect on head motion. These findings should be examined and replicated in other clinical populations.

Applying the Huntington's Disease Integrated Staging System (HD-ISS) to Observational Studies.

BACKGROUND: The Huntington's Disease Integrated Staging System (HD-ISS) has four stages that characterize disease progression. Classification is based on CAG length as a marker of Huntington's disease (Stage 0), striatum atrophy as a biomarker of pathogenesis (Stage 1), motor or cognitive deficits as HD signs and symptoms (Stage 2), and functional decline (Stage 3). One issue for implementation is the possibility that not all variables are measured in every study, and another issue is that the stages are broad and may benefit from progression subgrouping. OBJECTIVE: Impute stages of the HD-ISS for observational studies in which missing data precludes direct stage classification, and then define progression subgroups within stages. METHODS: A machine learning algorithm was used to impute stages. Agreement of the imputed stages with the observed stages was evaluated using graphical methods and propensity score matching. Subgroups were defined based on descriptive statistics and optimal cut-point analysis. RESULTS: There was good overall agreement between the observed stages and the imputed stages, but the algorithm tended to over-assign Stage 0 and under-assign Stage 1 for individuals who were early in progression. CONCLUSION: There is evidence that the imputed stages can be treated similarly to the observed stages for large-scale analyses. When imaging data are not available, imputation can be avoided by collapsing the first two stages using the categories of Stage≤1, Stage 2, and Stage 3. Progression subgroups defined within a stage can help to identify groups of more homogeneous individuals.

Who Are You? The Study of Personality in Patients With Anterograde Amnesia.

Little is known about the role of declarative memory in the ongoing perception of one's personality. Seven individuals who developed a rare and severe type of anterograde amnesia following damage to their medial temporal lobes were identified from our neurological patient registry. We examined the stability of their personality ratings on the Big Five Inventory over five retest periods and assessed the accuracy of their ratings via analyses of self-caregiver agreement. The patients portrayed a stable sense of self over the course of 1 year. However, their self-ratings differed from those provided by the caregivers. Intriguingly, these discrepancies diminished when caregivers retrospectively rated the patients' personalities prior to their brain injury, suggesting that patients' perceptions of themselves were stuck in the past. We interpret our findings to indicate that the ability to form new declarative memories is not required for maintaining a stable sense of self but may be important for updating one's sense of self over time.

Impact of binge drinking during college on resting state functional connectivity.

AIM: The current study examined the longitudinal effects of standard binge drinking (4+/5+ drinks for females/males in 2 hours) and extreme binge drinking (8+/10+ drinks for females/males in 2 hours) on resting-state functional connectivity. METHOD: 119 college students (61 males) were recruited in groups of distinct bingeing patterns at baseline: non-bingeing controls, standard and extreme bingers. Resting-state scans were first obtained when participants were freshmen/sophomores and again approximately two years later. Associations between longitudinal bingeing (reported during this two-year gap) and network connectivity were examined. Network connectivity was calculated by aggregating all edges affiliated with the same network (an edge is a functional connection between two brain regions). The relationship between longitudinal bingeing and connectivity edges was also studied using connectome-based predictive modeling (CPM). RESULTS: Greater standard bingeing was negatively associated with change in connectivity between Default Mode Network and Ventral Attention Network (DMN-VAN; False Discovery Rate corrected), controlling for initial binge groups, longitudinal network changes, motions, scanner, SES, sex, and age. The correlations between change in DMN-VAN connectivity and change in cognitive performance (Stroop, Digit Span, Letter Fluency, and Trail Making) were also tested, but the results were not significant. Lastly, CPM failed to identify a generalizable predictive model of longitudinal bingeing from change in connectivity edges. CONCLUSIONS: Binge drinking is associated with abnormality in networks implicated in attention and self-focused processes, which, in turn, have been implicated in rumination, craving, and relapse. More extensive alterations in functional connectivity might be observed with heavier or longer binge drinking pattern.

Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics.

BACKGROUND: A number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time. METHODS: We introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set. RESULTS: We present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern. CONCLUSION: Our proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.

Behavioral inhibition and reward processing in college binge drinkers with and without marijuana use.

AIM: Binge drinking is common during college, and studies have shown that many college students drink in quantities that far exceed the standard binge drinking threshold. Previous research has noted personality differences in individuals who engage in binge drinking, but few studies have examined neurobiological differences in both standard bingers (4/5 drinks in two hours for females/males; sBinge) and extreme binge drinkers (8+/10+ drinks in two hours for females/males; eBinge). METHOD: The current study of 221 college students used functional magnetic resonance imaging (fMRI) to study neural activation on a stop signal task (SST) to assess behavioral inhibition and a monetary incentive delay (MID) task to assess activation to rewards and losses. Non-bingeing controls, sBinge, and eBinge freshmen and sophomores were recruited. In addition, because binge/extreme binge drinking is often associated with marijuana (MJ) use, MJ + sBinge and MJ + eBinge groups were also included. RESULTS: All five groups showed strong activation in expected key cortical and striatal regions on both the SST and the MID. However, there were no significant differences between groups either at the whole-brain level or in specific regions of interest. Behavioral performance on the fMRI tasks also did not differ between groups. CONCLUSIONS: These results suggest that our sample of individuals who engage in binge or extreme binge drinking with or without MJ co-use do not differ in brain activity on reward and inhibitory tasks. Neural differences may be present on other cognitive tasks or may emerge later after more sustained use of alcohol, MJ, and other drugs.

Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality.

The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying components between hemispheres across subjects. SBL is successfully implemented with simulated data with inherent differences in laterality. SBL is then compared with a voxel-wise analysis utilizing structural data from a sample of patients with schizophrenia and controls without schizophrenia. SBL group comparisons identified three distinct temporal regions and one cerebellar region with significantly altered laterality in patients with schizophrenia relative to controls. Previous work highlights reductions in laterality (ie, reduced left gray matter volume) in patients with schizophrenia compared with controls without schizophrenia. Results from this pilot SBL project are the first, to our knowledge, to identify covarying laterality differences within discrete temporal brain regions. The authors argue SBL provides a unique focus to detect covarying laterality differences in patients with schizophrenia, facilitating the discovery of laterality aspects undetected in previous work.

Weighted average of shared trajectory: A new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time.

BACKGROUND: Dynamic functional network connectivity (dFNC) of the brain has attracted considerable attention recently. Many approaches have been suggested to study dFNC with sliding window Pearson correlation (SWPC) being the most well-known. SWPC needs a relatively large sample size to reach a robust estimation but using large window sizes prevents us to detect rapid changes in dFNC. NEW METHOD: Here we first calculate the gradients of each time series pair and use the magnitude of these gradients to calculate weighted average of shared trajectory (WAST) as a new estimator for dFNC. RESULTS: Using WAST to compare healthy control and schizophrenia patients using a large dataset, we show disconnectivity between different regions associated with schizophrenia. In addition, WAST results reveals patients with schizophrenia stay longer in a connectivity state with negative connectivity between motor and sensory regions than do healthy controls. COMPARISON WITH EXISTING METHODS: We compare WAST with SWPC and multiplication of temporal derivatives (MTD) using different simulation scenarios. We show that WAST enables us to detect very rapid changes in dFNC (undetected by SWPC) while MTD performance is generally lower. CONCLUSIONS: As large window sizes are unable to detect short states, using shorter window size is desirable if the estimator is robust enough. We provide evidence that WAST requires fewer samples (compared to SWPC) to reach a robust estimation. As a result, we were able to identify rapidly varying dFNC patterns undetected by SWPC while still being able to robustly estimate slower dFNC patterns.

Neurophysiological Correlates and Differential Drug Response in Subjects With a Family History of an Alcohol Use Disorder.

A family history of an alcohol use disorder (AUD) has been shown to increase one's risk of developing an AUD. Additionally, a positive family history of AUD (family history positive (FHP)) has neurobiological and neuropsychopharmacological consequences, and this review summarizes differential drug response as well as neuroanatomical and neurocognitive correlates. FHP status is related to altered responses to a number of drugs, including substances with abuse liability like alcohol, opioids, amphetamines, and ketamine. FHP individuals demonstrate fewer aversive effects and more rewarding response to both alcohol and subanesthetic dose ketamine. Ketamine is a rapid-acting antidepressant, and several studies have reported that ketamine is more effective for FHP treatment-resistant depressed individuals. In short, the reviewed neurophysiological differences may contribute to ketamine's enhanced antidepressant efficacy in FHP patients. Volumetric differences in the amygdala, nucleus accumbens, neocortex, and cerebellum are commonly reported. Furthermore, FHP has also been associated with altered neurocognitive performance, e.g., increased impulsivity. The imaging and psychological literature supports a neurodevelopmental lag hypothesis in FHP youth. The review will further discuss these findings in depth.

Altered Domain Functional Network Connectivity Strength and Randomness in Schizophrenia.

Functional connectivity is one of the most widely used tools for investigating brain changes due to schizophrenia. Previous studies have identified abnormal functional connectivity in schizophrenia patients at the resting state brain network level. This study tests the existence of functional connectivity effects at whole brain and domain levels. Domain level refers to the integration of data from several brain networks grouped by their functional relationship. Data integration provides more consistent and accurate information compared to an individual brain network. This work considers two domain level measures: functional connectivity strength and randomness. The first measure is simply an average of connectivities within the domain. The second measure assesses the unpredictability and lack of pattern of functional connectivity within the domain. Domains with less random connectivity have higher chance of exhibiting a biologically meaningful connectivity pattern. Consistent with prior observations, individuals with schizophrenia showed aberrant domain connectivity strength between subcortical, cerebellar, and sensorial brain areas. Compared to healthy volunteers, functional connectivity between cognitive and default mode domains showed less randomness, while connectivity between default mode-sensorial areas showed more randomness in schizophrenia patients. These differences in connectivity patterns suggest deleterious rewiring trade-offs among important brain networks.

Characterizing Whole Brain Temporal Variation of Functional Connectivity via Zero and First Order Derivatives of Sliding Window Correlations.

Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic states across several resting state experiments. However, estimation of states and their temporal dynamicity still suffers from noisy and imperfect estimations. In regular dFNC implementations, states are estimated by comparing connectivity patterns through the data without considering time, in other words only zero order changes are examined. In this work we propose a method that includes first order variations of dFNC in the searching scheme of dynamic connectivity patterns. Our approach, referred to as temporal variation of functional network connectivity (tvFNC), estimates the derivative of dFNC, and then searches for reoccurring patterns of concurrent dFNC states and their derivatives. The tvFNC method is first validated using a simulated dataset and then applied to a resting-state fMRI sample including healthy controls (HC) and schizophrenia (SZ) patients and compared to the standard dFNC approach. Our dynamic approach reveals extra patterns in the connectivity derivatives complementing the already reported state patterns. State derivatives consist of additional information about increment and decrement of connectivity among brain networks not observed by the original dFNC method. The tvFNC shows more sensitivity than regular dFNC by uncovering additional FNC differences between the HC and SZ groups in each state. In summary, the tvFNC method provides a new and enhanced approach to examine time-varying functional connectivity.

Personality traits and negative consequences associated with binge drinking and marijuana use in college students.

Background: Binge drinking is common in college students, and many drink in quantities greater than the standard definition of bingeing. Combined use of additional substances, particularly marijuana, is also common. Objectives: Increased impulsivity and sensation seeking are risk factors for bingeing, and this study was designed to characterize their association with extreme compared to standard bingeing, as well as with combined bingeing and marijuana use. Negative consequences of alcohol use were also investigated. Methods: Self-report personality measures and a measure of the negative consequences of alcohol use were given to a sample of 221 college students (109 females) sorted into a control and 4 binge groups based upon their patterns of bingeing and marijuana use. Narrowly defined, non-overlapping measures of impulsivity and sensation seeking were analyzed to assess the association of these personality measures with substance-use patterns and negative consequences of bingeing. Results: Standard bingers did not differ from non-bingeing controls on either impulsivity or sensation seeking, whereas extreme bingers had significantly higher impulsivity and sensation seeking scores than controls and also significantly higher sensation seeking than standard bingers. Exploratory analyses of a broader set of personality scales showed that a disinhibition scale was also significant predictor of substance use group. A number of personality traits significantly predicted substance use patterns as well as specific negative consequences of bingeing. Conclusions: Impulsivity, sensation seeking and disinhibition are significant associates of substance use patterns and the negative consequences of use in college students.

Autoconnectivity: A new perspective on human brain function.

BACKGROUND: Autocorrelation (AC) in fMRI time-series is a well-known phenomenon, typically attributed to colored noise and therefore removed from the data. We hypothesize that AC reflects systematic and meaningful signal fluctuations that may be tied to neural activity and provide evidence to support this hypothesis. NEW METHOD: Each fMRI time-series is modeled as an autoregressive process from which the autocorrelation is quantified. Then, autocorrelation during resting-state fMRI and auditory oddball (AOD) task in schizophrenia and healthy volunteers is examined. RESULTS: During resting-state, AC was higher in the visual cortex while during AOD task, frontal part of the brain exhibited higher AC in both groups. AC values were significantly lower in specific brain regions in schizophrenia patients (such as thalamus during resting-state) compared to healthy controls in two independent datasets. Moreover, AC values had significant negative correlation with patients' symptoms. AC differences discriminated patients from healthy controls with high accuracy (resting-state). COMPARISON WITH EXISTING METHODS: Contrary to most prior works, the results suggest AC shows meaningful patterns that are discriminative between patients and controls. Our results are in line with recent works attributing autocorrelation to feedback loop of brain's regulatory circuit. CONCLUSIONS: Autoconnectivity is cognitive state dependent (resting-state vs. task) and mental state dependent (healthy vs. schizophrenia). The concept of autoconnectivity resembles a recurrent neural network and provides a new perspective of functional integration in the brain. These findings may have important implications for understanding of brain function in health and disease as well as for analysis of fMRI time-series.

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration.

The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra- and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi-site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel-wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within- and between-subject spatial variability.

Association Between Age and Familial Risk for Alcoholism on Functional Connectivity in Adolescence.

OBJECTIVE: Youth with a family history of alcohol use disorder (family history positive [FHP]) are at increased risk for developing maladaptive substance use relative to family history negative (FHN) peers. Building on earlier studies demonstrating morphological differences and distinct patterns of neural activation in FHP, the purpose of the present study was to investigate differential intrinsic functional connectivity among brain networks indexing premorbid risk of developing alcohol use disorder (AUD). METHOD: The current study examined intrinsic functional connectivity using resting state functional magnetic resonance imaging in 191 adolescents 13 to 18 years of age with and without family history of AUD via independent component analysis, a method enabling data-driven investigation of internetwork and intranetwork connectivity among brain regions at rest. RESULTS: Analyses revealed significantly lower intranetwork connectivity in FHP compared to FHN participants between the dorsal premotor cortex and other sensorimotor network regions. Reduced intranetwork connectivity in this region was further correlated with the number of biological family members with AUD and mood disorders. Robust differences were also evident in internetwork connectivity as a function of age. However, there was no evidence for family history by age interactions. CONCLUSION: Intra- but not internetwork connectivity appears to differentiate FHP and FHN adolescents, whereas age differences within adolescence are marked by differences in internetwork connectivity.

Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function.

The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FDs), as well as temporal dynamics within and between FDs. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high-order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations, from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g., the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variations provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.

Polygenic risk score, genome-wide association, and gene set analyses of cognitive domain deficits in schizophrenia.

This study assessed genetic contributions to six cognitive domains, identified by the MATRICS Cognitive Consensus Battery as relevant for schizophrenia, cognition-enhancing, clinical trials. Psychiatric Genomics Consortium Schizophrenia polygenic risk scores showed significant negative correlations with each cognitive domain. Genome-wide association analyses identified loci associated with attention/vigilance (rs830786 within HNF4G), verbal memory (rs67017972 near NDUFS4), and reasoning/problem solving (rs76872642 within HDAC9). Gene set analysis identified unique and shared genes across cognitive domains. These findings suggest involvement of common and unique mechanisms across cognitive domains and may contribute to the discovery of new therapeutic targets to treat cognitive deficits in schizophrenia.

Cortical Thickness in Adolescents with a Family History of Alcohol Use Disorder.

BACKGROUND: Individuals with a family history (FH+) of alcohol use disorder (AUD) have a higher risk for developing an AUD than those with no family history (FH-) of AUD. In addition, FH+ individuals tend to perform worse on neuropsychological measures and show heightened impulsivity, which may be due to underlying differences in brain structure such as cortical thickness. The primary aim of this study was to investigate differences in cortical thickness in FH+ compared to FH- adolescents. Secondary aims were to (i) investigate differences in executive functioning and impulsivity, and (ii) examine associations between brain structure and behavior. METHODS: Brain scans of 95 FH- and 93 FH+ subjects aged 13 to 18 were obtained using magnetic resonance imaging. FH+ subjects were required to have at least 1 biological parent with a history of an AUD. FH+ and FH- individuals had limited or no past alcohol use, thereby minimizing potential effects of alcohol. Subjects were evaluated on impulsivity and executive functioning tasks. Thicknesses of cortical lobes and subregions were analyzed using FreeSurfer. Regions showing group differences were examined for group-by-age interactions and correlations with neuropsychological and personality measures. RESULTS: FH+ adolescents had thinner cortices in frontal and parietal lobes, notably in the medial orbitofrontal, lateral orbitofrontal, and superior parietal cortices. The difference in cortical thickness between family history groups was strongest among the youngest subjects. FH+ subjects were also more impulsive and had poorer performance on a spatial memory task. CONCLUSIONS: These findings demonstrate frontal and parietal structural differences in FH+ adolescents that might underlie cognitive and behavioral characteristics associated with AUD risk.

Robust automated constellation-based landmark detection in human brain imaging.

A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses the knowledge of each additional detected landmark as an improved foundation for improved prediction of the next landmark location. The accuracy and robustness of the presented method was evaluated by comparing the automatically generated results to two manual raters on 30 identified landmark points extracted from each of 30 T1-weighted magnetic resonance images. For the landmarks with unambiguous anatomical definitions, the average discrepancy between the algorithm results and each human observer differed by less than 1 mm from the average inter-observer variability when the algorithm was evaluated on imaging data collected from the same site as the model building data. Similar results were obtained when the same model was applied to a set of heterogeneous image volumes from seven different collection sites representing 3 scanner manufacturers. This method is reliable for general application in large-scale multi-site studies that consist of a variety of imaging data with different orientations, spacings, origins, and field strengths.

Neuropsychological profile in adult schizophrenia measured with the CMINDS.

Schizophrenia neurocognitive domain profiles are predominantly based on paper-and-pencil batteries. This study presents the first schizophrenia domain profile based on the Computerized Multiphasic Interactive Neurocognitive System (CMINDS(®)). Neurocognitive domain z-scores were computed from computerized neuropsychological tests, similar to those in the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery (MCCB), administered to 175 patients with schizophrenia and 169 demographically similar healthy volunteers. The schizophrenia domain profile order by effect size was Speed of Processing (d=-1.14), Attention/Vigilance (d=-1.04), Working Memory (d=-1.03), Verbal Learning (d=-1.02), Visual Learning (d=-0.91), and Reasoning/Problem Solving (d=-0.67). There were no significant group by sex interactions, but overall women, compared to men, showed advantages on Attention/Vigilance, Verbal Learning, and Visual Learning compared to Reasoning/Problem Solving on which men showed an advantage over women. The CMINDS can readily be employed in the assessment of cognitive deficits in neuropsychiatric disorders; particularly in large-scale studies that may benefit most from electronic data capture.