Context

Hoarding disorder (HD), previously considered a subtype of obsessive-compulsive disorder (OCD), has been proposed as a unique diagnostic entity in DSM-5. Current models of HD emphasize problems of decision-making, attachment to possessions, and poor insight, whereas previous neuroimaging studies have suggested abnormalities in frontal brain regions.

Objective

To examine the neural mechanisms of impaired decision making in HD in patients with well-defined primary HD compared with patients with OCD and healthy control subjects (HCs).

Design

We compared neural activity among patients with HD, patients with OCD, and HCs during decisions to keep or discard personal possessions and control possessions from November 9, 2006, to August 13, 2010.

Setting

Private, not-for-profit hospital.

Participants

A total of 107 adults (43 with HD, 31 with OCD, and 33 HCs).

Main Outcome Measures

Neural activity as measured by functional magnetic resonance imaging in which actual real-time and binding decisions had to be made about whether to keep or discard possessions.

Results

Compared with participants with OCD and HC, participants with HD exhibited abnormal activity in the anterior cingulate cortex and insula that was stimulus dependent. Specifically, when deciding about items that did not belong to them, patients with HD showed relatively lower activity in these brain regions. However, when deciding about items that belonged to them, these regions showed excessive functional magnetic resonance imaging signals compared with the other 2 groups. These differences in neural function correlated significantly with hoarding severity and self-ratings of indecisiveness and “not just right” feelings among patients with HD and were unattributable to OCD or depressive symptoms.

Conclusions

Findings suggest a biphasic abnormality in anterior cingulate cortex and insula function in patients with HD related to problems in identifying the emotional significance of a stimulus, generating appropriate emotional response, or regulating affective state during decision making.

Background

Although cocaine dependence involves abnormalities in drug-related reward-based decision-making, it is not well understood whether these abnormalities generalize to non-drug-related cues and rewards, and how neural functions underlying reward processing in cocaine abusers relate to treatment outcome.

Methods

Twenty cocaine dependent (CD) patients before treatment and 20 matched healthy control (HC) subjects participated in fMRI while performing a Monetary Incentive Delay Task (MIDT). Outcomes through eight weeks were assessed via percent cocaine-negative urine toxicology, self-reported cocaine abstinence, and treatment retention.

Results

Amongst the whole sample, anticipation of working for monetary reward (i.e., reward anticipation) was associated with activation in the ventral striatum (VS), medial frontal gyrus, thalamus, right subcallosal gyrus, right insula, and left amygdala. CD as compared with HC participants exhibited greater activation during notification of rewarding outcome (i.e., reward receipt) in left and right VS, right caudate, and right insula. In CD participants during reward anticipation, activation in left and right thalamus and right caudate correlated negatively with percent cocaine-negative urine toxicology, activation in thalamus bilaterally correlated negatively with self-reported abstinence measures, and activation in left amygdala and parahippocampal gyrus correlated negatively with treatment retention. During reward notification, activation in right thalamus, right VS and left culmen correlated negatively with abstinence and with urine toxicology.

Conclusions

These findings suggest that in treatment-seeking CD participants, cortico-limbic reward circuitry is relatively over-activated during MIDT performance and specific regional activations related to reward processing may predict aspects of treatment outcome and represent important targets for treatment development in CD.

Individual differences in behavioral inhibition and behavioral activation may place certain people at greater risk for neuropsychiatric disorders and engagement in risky behaviors. Therefore, studying the neural correlates of behavioral inhibition and activation may help us understand neural mechanisms underlying risk behaviors in both clinical and non-clinical populations. To investigate, we assessed the relationships between white matter integrity and measures of behavioral inhibition and behavioral activation in 51 healthy participants using diffusion tensor imaging (DTI) and the Behavioral Inhibition System/Behavioral Activation System (BIS/BAS) scale. Scores on the Fun-Seeking subscale of the BAS positively correlated with DTI fractional anisotropy (FA) in the left corona radiata and adjacent superior longitudinal fasciculus, and with mean diffusivity (MD) in the left inferior longitudinal fasciculus and inferior fronto-occipital fasciculus after controlling for age, gender, and education. These findings suggest that the integrity of white matter connecting extensive brain regions implicated in self-control and the processing of rewards and emotions are associated with individual differences in the motivation for seeking and participating in fun and novel experiences.

Impulsivity, often defined as a human behavior characterized by the inclination of an individual to act on urge rather than thought, with diminished regard to consequences, encompasses a range of maladaptive behaviors which are in turn affected by distinct neural systems. Congruent with the above definition, behavioral studies have consistently shown that the underlying construct of impulsivity is multidimensional in nature. However, research to date has been inconclusive regarding the different domains or constructs that constitute this behavior. In addition there is also no clear consensus as to whether self-report and laboratory based measures of impulsivity measure the same or different domains. The current study aimed to: 1) characterize the underlying multidimensional construct of impulsivity using a sample with varying degrees of putative impulsivity related to substance misuse, including subjects who were at-risk of substance use or addicted (ARA), and 2) assess relationships between self-report and laboratory measures of impulsivity, using a principal component-based factor analysis. In addition, our supplementary goal was to evaluate the structural constructs of impulsivity within each group separately (healthy and ARA). We used five self-report measures (Behavioral Inhibition System/Behavioral Activation System (BIS/BAS), Barratt Impulsivity Scale-11, Padua Inventory, Zuckerman Sensation Seeking Scale (SSS), and Sensitivity to Punishment and Sensitivity to Reward Questionnaire) and two computer based laboratory tasks (Balloon Analog Risk Task and the Experiential Delay Task) to measure aspects of impulsivity in a total of 176 adult subjects. Subjects included healthy controls (N=89), non-alcoholic subjects with family histories of alcoholism (FHP; N=36) and both former (N=20) and current (N=31) cocaine users. Subjects with a family history of alcoholism and cocaine abusers were grouped together as “at-risk/addicted” (ARA) to evaluate our supplementary goal. Our overall results revealed the multidimensional nature of the impulsivity construct as captured optimally through a five factor solution that accounted for nearly 70% of the total variance. The five factors/components were imputed as follows “Self-Reported Behavioral Activation”, “Self-Reported Compulsivity and Reward/Punishment”, “Self-Reported Impulsivity”, “Behavioral Temporal Discounting” and “Behavioral Risk-Taking.” We also found that contrary to previously published reports, there was significant overlap between certain laboratory and self-report measures, indicating that they might be measuring the same impulsivity domain. In addition, our supplemental analysis also suggested that the impulsivity constructs were largely, but not entirely the same within the healthy and ARA groups.

The anterior cingulate and a collection of other prefrontal and parietal brain regions are implicated in error processing and cognitive control. The effects of different doses of alcohol on activity within these brain regions during an fMRI task where errors are frequently committed have not been fully explored. This study examined the impact of a placebo [Breath Alcohol Concentration (BrAC) = 0.00%], moderate (BrAC = 0.05%) and high (BrAC = 0.10%) doses of alcohol on brain hemodynamic activity during a functional MRI (fMRI) Go/No-Go task in thirty-eight healthy volunteers. Alcohol increased reaction time and false alarm errors in a dose-dependent manner. FMRI analyses showed alcohol decreased activity in anterior cingulate, lateral prefrontal cortex, insula and parietal lobe regions during false alarm responses to No-Go stimuli. These findings indicate that brain regions implicated in error processing are affected by alcohol and might provide a neural basis for alcohol's effects on behavioral performance.

Schizophrenia (SZ) is one of the most cryptic and costly mental disorders in terms of human suffering and societal expenditure (van Os and Kapur, 2009). Though strong evidence for functional, structural, and genetic abnormalities associated with this disease exists, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009), and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a). It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a). It is becoming increasingly clear that multimodal fusion, a technique which takes advantage of the fact that each modality provides a limited view of the brain/gene and may uncover hidden relationships, is an important tool to help unravel the black box of schizophrenia. In this review paper, we survey a number of multimodal fusion applications which enable us to study the schizophrenia macro-connectome, including brain functional, structural, and genetic aspects and may help us understand the disorder in a more comprehensive and integrated manner. We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research question.

We present a novel method to extract classification features from functional magnetic resonance imaging (fMRI) data collected at rest or during the performance of a task. By combining a two-level feature identification scheme with kernel principal component analysis (KPCA) and Fisher’s linear discriminant analysis (FLD), we achieve high classification rates in discriminating healthy controls from patients with schizophrenia. Experimental results using leave-one-out cross-validation show that features extracted from the default mode network (DMN) lead to a classification accuracy of over 90% in both data sets. Moreover, using a majority vote method that uses multiple features, we achieve a classification accuracy of 98% in auditory oddball (AOD) task and 93% in rest data. Several components, including DMN, temporal, and medial visual regions, are consistently present in the set of features that yield high classification accuracy. The features we have extracted thus show promise to be used as biomarkers for schizophrenia. Results also suggest that there may be different advantages to using resting fMRI data or task fMRI data.

Openness is a personality trait that has been linked to intelligence and divergent thinking. DeYoung, Peterson, and Higgins (2005) theorized that trait Openness depends on dopamine function, especially in the prefrontal cortex. We tested their theory in 335 healthy adults by hypothesizing that individual differences in Openness would correlate more strongly with performance on tests of executive function than on tests of intelligence and fluency. However, Openness correlated more strongly with verbal/crystallized intelligence (Gc; r=0.44) than with executive functioning (r=0.16) and fluency (r=0.24). Further, the partial correlation between Openness and Gc increased from r=0.26 among young adults to r=0.53 among elderly adults. These findings suggest that Openness is more closely associated with the acquisition of broad verbal intellectual skills and knowledge than with executive abilities localized to a specific brain region or neurotransmitter system.

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in patients versus controls. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject’s ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients.

Despite the knowledge that many drugs affect men and women differently, few studies exploring the effects of marijuana use on cognition have included women. Findings from both animal and human studies suggest marijuana may have more marked effects in women. This study examined sex differences in the acute effects of marijuana on cognition in 70 (n= 35 male, 35 female) occasional users of marijuana. Tasks were chosen to tap a wide variety of cognitive domains affected by sex and/or marijuana including attention, cognitive flexibility, time estimation, and visuospatial processing. As expected, acute marijuana use impaired performance on selective and divided attention, time estimation, and cognitive flexibility. While there did not appear to be sex differences in marijuana's effects on cognition, women requested to discontinue the smoking session more often than men likely leading to an underestimation of differences. Further study of psychological differences in marijuana's effects on men and women following both acute and residual effects of marijuana is warranted.

Autism spectrum disorders (ASDs) are characterized by deficits in social and communication processes. Recent data suggest that altered functional connectivity (FC), i.e. synchronous brain activity, might contribute to these deficits. Of specific interest is the FC integrity of the default mode network (DMN), a network active during passive resting states and cognitive processes related to social deficits seen in ASD, e.g. Theory of Mind. We investigated the role of altered FC of default mode sub-networks (DM-SNs) in 16 patients with high-functioning ASD compared to 16 matched healthy controls of short resting fMRI scans using independent component analysis (ICA). ICA is a multivariate data-driven approach that identifies temporally coherent networks, providing a natural measure of FC. Results show that compared to controls, patients showed decreased FC between the precuneus and medial prefrontal cortex/anterior cingulate cortex, DMN core areas, and other DM-SNs areas. FC magnitude in these regions inversely correlated with the severity of patients' social and communication deficits as measured by the Autism Diagnostic Observational Schedule and the Social Responsiveness Scale. Importantly, supplemental analyses suggest that these results were independent of treatment status. These results support the hypothesis that DM-SNs under-connectivity contributes to the core deficits seen in ASD. Moreover, these data provide further support for the use of data-driven analysis with resting-state data for illuminating neural systems that differ between groups. This approach seems especially well suited for populations where compliance with and performance of active tasks might be a challenge, as it requires minimal cooperation.

Background:

Driving while under the influence of alcohol is a major public health problem whose neural basis is not well understood. In a recently published fMRI study (Meda et al, 2009), our group identified five, independent critical driving-associated brain circuits whose inter-regional connectivity was disrupted by alcohol intoxication. However, the functional connectivity between these circuits has not yet been explored in order to determine how these networks communicate with each other during sober and alcohol-intoxicated states.

Methods:

In the current study, we explored such differences in connections between the above brain circuits and driving behavior, under the influence of alcohol versus placebo. Forty social drinkers who drove regularly underwent fMRI scans during virtual reality driving simulations following two alcohol doses, placebo and an individualized dose producing blood alcohol concentrations (BACs) of 0.10%.

Results:

At the active dose, we found specific disruptions of functional network connectivity between the frontal-temporal-basal ganglia and the cerebellar circuits. The temporal connectivity between these two circuits was found to be less correlated (p <0.05) when driving under the influence of alcohol. This disconnection was also associated with an abnormal driving behavior (unstable motor vehicle steering).

Conclusions:

Connections between frontal-temporal-basal ganglia and cerebellum have recently been explored; these may be responsible in part for maintaining normal motor behavior by integrating their overlapping motor control functions. These connections appear to be disrupted by alcohol intoxication, in turn associated with an explicit type of impaired driving behavior.

Previous studies have reported learning and navigation impairments in schizophrenia patients during virtual reality allocentric learning tasks. The neural bases of these deficits have not been explored using functional MRI despite well-explored anatomic characterization of these paradigms in non-human animals. Our objective was to characterize the differential distributed neural circuits involved in virtual Morris water task performance using independent component analysis (ICA) in schizophrenia patients and controls. Additionally, we present behavioral data in order to derive relationships between brain function and performance, and we have included a general linear model-based analysis in order to exemplify the incremental and differential results afforded by ICA. Thirty-four individuals with schizophrenia and twenty-eight healthy controls underwent fMRI scanning during a block design virtual Morris water task using hidden and visible platform conditions. Independent components analysis was used to deconstruct neural contributions to hidden and visible platform conditions for patients and controls. We also examined performance variables, voxel-based morphometry and hippocampal subparcellation, and regional BOLD signal variation. Independent component analysis identified five neural circuits. Mesial temporal lobe regions, including the hippocampus, were consistently task-related across conditions and groups. Frontal, striatal, and parietal circuits were recruited preferentially during the visible condition for patients, while frontal and temporal lobe regions were more saliently recruited by controls during the hidden platform condition. Gray matter concentrations and BOLD signal in hippocampal subregions were associated with task performance in controls but not patients. Patients exhibited impaired performance on the hidden and visible conditions of the task, related to negative symptom severity. While controls showed coupling between neural circuits, regional neuroanatomy, and behavior, patients activated different task-related neural circuits, not associated with appropriate regional neuroanatomy. GLM analysis elucidated several comparable regions, with the exception of the hippocampus. Inefficient allocentric learning and memory in patients may be related to an inability to recruit appropriate task-dependent neural circuits.

In the United States, one in six teenagers has driven under the influence of marijuana. Driving under the influence of marijuana and alcohol is equally prevalent, despite the fact that marijuana use is less common than alcohol use. Much of the research examining the effects of marijuana on driving performance was conducted in the 1970s and led to equivocal findings. During that time, few studies included women and driving simulators were rudimentary. Further, the potency of marijuana commonly used recreationally has increased. This study examined sex differences in the acute effects of marijuana on driving performance using a realistic, validated driving simulator. Eighty-five subjects (n = 50 males, 35 females) participated in this between-subjects, double-blind, placebo controlled study. In addition to an uneventful, baseline segment of driving, participants were challenged with collision avoidance and distracted driving scenarios. Under the influence of marijuana, participants decreased their speed and failed to show expected practice effects during a distracted drive. No differences were found during the baseline driving segment or collision avoidance scenarios. No differences attributable to sex were observed. This study enhances the current literature by identifying distracted driving and the integration of prior experience as particularly problematic under the influence of marijuana.

Diabetes is associated with dementia in older adults, but it remains unclear whether nondemented adults with type 2 diabetes show subtle abnormalities across cognition, neuroanatomy, and everyday functioning. Using the Aging, Brain Imaging, and Cognition study sample of 301 community-dwelling, middle-aged and older adults, we conducted a secondary analysis on 28 participants with and 150 participants without diabetes. We analyzed brain magnetic resonance imaging data, cognitive test performance, and informant ratings of personal and instrumental activities of daily living (PADL/IADL). Relative to controls, participants with diabetes had lower brain-to-intracranial volume ratios (69.3 ± 4.5% vs. 71.7 ± 4.6%; p < .02), and performed more poorly on measures of working memory, processing speed, fluency, and crystallized intelligence (all p <.05). Decrements in working memory and processing speed were associated with IADL limitations (p < .01). Nondemented adults with diabetes exhibit neuroanatomic and cognitive abnormalities. Their cognitive deficits correlate with everyday functional limitations.

The cognitive deficits associated with schizophrenia are commonly believed to arise from the abnormal temporal integration of information, however a quantitative approach to assess network coordination is lacking. Here, we propose to use cross-frequency modulation (cfM), the dependence of local high-frequency activity on the phase of widespread low-frequency oscillations, as an indicator of network coordination and functional integration. In an exploratory analysis based on pre-existing data, we measured cfM from multi-channel EEG recordings acquired while schizophrenia patients (n = 47) and healthy controls (n = 130) performed an auditory oddball task. Novel application of independent component analysis (ICA) to modulation data delineated components with specific spatial and spectral profiles, the weights of which showed covariation with diagnosis. Global cfM was significantly greater in healthy controls (F1,175 = 9.25, P < 0.005), while modulation at fronto-temporal electrodes was greater in patients (F1,175 = 17.5, P < 0.0001). We further found that the weights of schizophrenia-relevant components were associated with genetic polymorphisms at previously identified risk loci. Global cfM decreased with copies of 957C allele in the gene for the dopamine D2 receptor (r = −0.20, P < 0.01) across all subjects. Additionally, greater “aberrant” fronto-temporal modulation in schizophrenia patients was correlated with several polymorphisms in the gene for the α2-subunit of the GABAA receptor (GABRA2) as well as the total number of risk alleles in GABRA2 (r = 0.45, P < 0.01). Overall, our results indicate great promise for this approach in establishing patterns of cfM in health and disease and elucidating the roles of oscillatory interactions in functional connectivity.

Objective

In this paper, we develop a dynamic functional network connectivity (FNC) analysis approach using correlations between windowed time-courses of different brain networks (components) estimated via spatial independent component analysis (sICA). We apply the developed method to fMRI data to evaluate it and to study task-modulation of functional connections.

Materials and methods

We study the theoretical basis of the approach, perform a simulation analysis and apply it to fMRI data from schizophrenia patients (SP) and healthy controls (HC). Analyses on the fMRI data include: (a) group sICA to determine regions of significant task-related activity, (b) static and dynamic FNC analysis among these networks by using maximal lagged-correlation and time–frequency analysis, and (c) HC–SP group differences in functional network connections and in task-modulation of these connections.

Results

This new approach enables an assessment of task-modulation of connectivity and identifies meaningful inter-component linkages and differences between the two study groups during performance of an auditory oddball task (AOT). The static FNC results revealed that connectivities involving medial visual–frontal, medial temporal–medial visual, parietal–medial temporal, parietal–medial visual and medial temporal–anterior temporal were significantly greater in HC, whereas only the right lateral fronto-parietal (RLFP)–orbitofrontal connection was significantly greater in SP. The dynamic FNC revealed that task-modulation of motor–frontal, RLFP–medial temporal and posterior default mode (pDM)–parietal connections were significantly greater in SP, and task modulation of orbitofrontal–pDM and medial temporal–frontal connections were significantly greater in HC (all P < 0.05).

Conclusion

The task-modulation of dynamic FNC provided findings and differences between the two groups that are consistent with the existing hypothesis that schizophrenia patients show fewer segregated motor, sensory, cognitive functions and less default mode network activity when engaged with a task. Dynamic FNC, based on sICA, provided additional results which are different than, but complementary to, those of static FNC. For example, it revealed dynamic changes in default mode network connectivities with other regions which were significantly different in schizophrenia in terms of task-modulation, findings which were not possible to detect by static FNC.

A fundamental, yet rarely tested premise of developmental cognitive neuroscience is that changes in brain activity and improvements in behavioral control across adolescent development are related to brain maturational factors that shape a more efficient, highly-interconnected brain in adulthood. We present the first multimodal neuroimaging study to empirically demonstrate that maturation of executive cognitive ability is directly associated with the relationship of white matter development and age-related changes in neural network functional integration. In this study, we identified specific white matter regions whose maturation across adolescence appears to reduce reliance on local processing in brain regions recruited for conscious, deliberate cognitive control in favor of a more widely distributed profile of functionally-integrated brain activity. Greater white matter coherence with age was associated with both increases and decreases in functional connectivity within task-engaged functional circuits. Importantly, these associations between white matter development and brain system functional integration were related to behavioral performance on tests of response inhibition, demonstrating their importance in the maturation of optimal cognitive control.

Background

Schizophrenia is a complex genetic disorder, with multiple putative risk genes and many reports of reduced cortical gray matter. Identifying the genetic loci contributing to these structural alterations in schizophrenia (and likely also to normal structural gray matter patterns) could aid understanding of schizophrenia’s pathophysiology. We used structural parameters as potential intermediate illness markers to investigate genomic factors derived from single nucleotide polymorphism (SNP) arrays.

Method

We used research quality structural magnetic resonance imaging (sMRI) scans from European American subjects including 33 healthy control subjects and 18 schizophrenia patients. All subjects were genotyped for 367 SNPs. Linked sMRI and genetic (SNP) components were extracted to reveal relationships between brain structure and SNPs, using parallel independent component analysis, a novel multivariate approach that operates effectively in small sample sizes.

Results

We identified an sMRI component that significantly correlated with a genetic component (r = −.536, p < .00005); components also distinguished groups. In the sMRI component, schizophrenia gray matter deficits were in brain regions consistently implicated in previous reports, including frontal and temporal lobes and thalamus (p < .01). These deficits were related to SNPs from 16 genes, several previously associated with schizophrenia risk and/or involved in normal central nervous system development, including AKT, PI3K, SLC6A4, DRD2, CHRM2, and ADORA2A.

Conclusions

Despite the small sample size, this novel analysis method identified an sMRI component including brain areas previously reported to be abnormal in schizophrenia and an associated genetic component containing several putative schizophrenia risk genes. Thus, we identified multiple genes potentially underlying specific structural brain abnormalities in schizophrenia.

Functional network connectivity (FNC) is an approach that examines the relationships between brain networks (as opposed to functional connectivity (FC) that focuses upon the relationships between single voxels). FNC may help explain the complex relationships between distributed cerebral sites in the brain and possibly provide new understanding of neurological and psychiatric disorders such as schizophrenia. In this paper, we use independent component analysis (ICA) to extract the time courses of spatially independent components and then use these in Granger causality test (GCT) to investigate causal relationships between brain activation networks. We present results using both simulations and fMRI data of 155 subjects obtained during two different tasks. Unlike previous research, causal relationships are presented over different portions of the frequency spectrum in order to differentiate high and low frequency effects and not merged in a scalar. The results obtained using Sternberg item recognition paradigm (SIRP) and auditory oddball (AOD) tasks showed FNC differentiations between schizophrenia and control groups, and explained how the two groups differed during these tasks. During the SIRP task, secondary visual and cerebellum activation networks served as hubs and included most complex relationships between the activated regions. Secondary visual and temporal lobe activations replaced these components during the AOD task.

White matter hyperintensities (WMH) can compromise cognition in older adults, but differences in sampling, WMH measurements, and cognitive assessments contribute to discrepant findings across studies. We examined linear and nonlinear effects of WMH volumes on cognition in 253 reasonably healthy adults. After adjusting for demographic characteristics and total brain volumes, WMH burden was not associated with cognition in those aged 20–59. In participants aged 60 and older, models accounted for ≥58% of the variance in performance on tests of working memory, processing speed, fluency, and fluid intelligence, and WMH volumes accounted for variance beyond that explained by age and other demographic characteristics. Larger increases in WMH burden over 5 years also were associated with steeper cognitive declines over the same interval. Results point to both age-related and age-independent effects of WMH on cognition in later life and suggest that the accumulation of WMH might partially explain normal age-related declines in cognition.

Background

Given the wealth of data in the literature on schizophrenia endophenotypes, it is useful to have one source to reference their frequency data. We reviewed the literature on disease-liability associated variants in structural and functional magnetic resonance images (MRI), sensory processing measures, neuromotor abilities, neuropsychological measures, and physical characteristics in schizophrenia patients (SCZ), their first-degree relatives (REL), and healthy controls (HC). The purpose of this review was to provide a summary of the existing data on the most extensively published endophenotypes for schizophrenia.

Methods

We searched PubMed and MedLine for all studies on schizophrenia endophenotypes comparing SCZ to HC and/or REL to HC groups. Percent abnormal values, generally defined as > 2 SD from the mean (in the direction of abnormality) and/or associated effect sizes (Cohen’s d) were calculated foreach study.

Results

Combined, the articles reported an average 39.4% (SD=20.7%; range=2.2-100%) of abnormal values in SCZ, 28.1% (SD=16.6%; range=1.6-67.0%) abnormal values in REL, and 10.2% (SD=6.7%; range=0.0-34.6%) in HC groups.

Conclusions

These findings are reviewed in the context of emerging hypotheses on schizophrenia endophenotypes, as well as a discussion of clustering trends among the various intermediate phenotypes. In addition, programs for future research are discussed, as instantiated in a few recent large-scale studies on multiple endophenotypes across patients, relatives, and healthy controls.

Studies demonstrating selective brain networks subserving motivation and mentalization (i.e. attributing states of mind to others) during social interactions have not investigated their mutual independence. We report the results of two fMRI studies using a competitive game requiring players to use implicit ‘on-line’ mentalization simultaneously with motivational processes of gains and losses in playing against a human or a computer opponent. We delineate a network, consisting of bilateral temporoparietal junction, temporal pole (TP), medial prefrontal cortex (MPFC) and right fusiform gyrus, which is sensitive to the opponent's response (challenging>not challenging the player) and opponent type (human>computer). This network is similar to a known explicit ‘off-line’ mentalization circuit, suggesting its additional involvement in implicit ‘on-line’ mentalization, a process more applicable to real-life social interactions. Importantly, only MPFC and TP were selective to mentalization compared to motivation, highlighting their specific operation in attributing states of mind to others during social interactions.

Background

Numerous neuroimaging studies report abnormal regional brain activity during working memory performance in schizophrenia, but few have examined brain network integration as determined by “functional connectivity” analyses.

Methodology/Principal Findings

We used independent component analysis (ICA) to identify and characterize dysfunctional spatiotemporal networks in schizophrenia engaged during the different stages (encoding and recognition) of a Sternberg working memory fMRI paradigm. 37 chronic schizophrenia and 54 healthy age/gender-matched participants performed a modified Sternberg Item Recognition fMRI task. Time series images preprocessed with SPM2 were analyzed using ICA. Schizophrenia patients showed relatively less engagement of several distinct “normal” encoding-related working memory networks compared to controls. These encoding networks comprised 1) left posterior parietal-left dorsal/ventrolateral prefrontal cortex, cingulate, basal ganglia, 2) right posterior parietal, right dorsolateral prefrontal cortex and 3) default mode network. In addition, the left fronto-parietal network demonstrated a load-dependent functional response during encoding. Network engagement that differed between groups during recognition comprised the posterior cingulate, cuneus and hippocampus/parahippocampus. As expected, working memory task accuracy differed between groups (p<0.0001) and was associated with degree of network engagement. Functional connectivity within all three encoding-associated functional networks correlated significantly with task accuracy, which further underscores the relevance of abnormal network integration to well-described schizophrenia working memory impairment. No network was significantly associated with task accuracy during the recognition phase.

Conclusions/Significance

This study extends the results of numerous previous schizophrenia studies that identified isolated dysfunctional brain regions by providing evidence of disrupted schizophrenia functional connectivity using ICA within widely-distributed neural networks engaged for working memory cognition.

Synaptic development and elimination are normal neurodevelopmental processes which if altered could contribute to various neuropsychiatric disorders. 31P-1H magnetic resonance spectroscopic imaging and structural MRI exams were conducted on 106 healthy children ages 6–18 years in order to identify neuromolecular indices of synaptic development and elimination. Over the age range studied, age-related changes in high-energy phosphate (phosphocreatine), membrane phospholipid metabolism (precursors and breakdown products), and gray matter were found. These neuromolecular and structural indices of synaptic development and elimination are associated with development of several cognitive domains and changes in gray matter volume. Monitoring of these molecular markers is essential for devising treatment strategies for neurodevelopmental disorders.