Time to Functional Loss as an Endpoint in Huntington's Disease Trials: Enrichment and Sample Size.

BACKGROUND: Clinical trial scenarios can be modeled using data from observational studies, providing critical information for design of real-world trials. The Huntington's Disease Integrated Staging System (HD-ISS) characterizes disease progression over an individual's lifespan and allows for flexibility in the design of trials with the goal of delaying progression. Enrichment methods can be applied to the HD-ISS to identify subgroups requiring smaller estimated sample sizes. OBJECTIVE: Investigate time to the event of functional decline (HD-ISS Stage 3) as an endpoint for trials in HD and present sample size estimates after enrichment. METHODS: We classified individuals from observational studies according to the HD-ISS. We assessed the ability of the prognostic index normed (PIN) and its components to predict time to HD-ISS Stage 3. For enrichment, we formed groups from deciles of the baseline PIN distribution for HD-ISS Stage 2 participants. We selected enrichment subgroups closer to Stage 3 transition and estimated sample sizes, using delay in the transition time as the effect size. RESULTS: In predicting time to HD-ISS Stage 3, PIN outperforms its components. Survival curves for each PIN decile show that groups with PIN from 1.48 to 2.74 have median time to Stage 3 of approximately 2 years and these are combined to create enrichment subgroups. Sample size estimates are presented by enrichment subgroup. CONCLUSIONS: PIN is predictive of functional decline. A delay of 9 months or more in the transition to Stage 3 for an enriched sample yields feasible sample size estimates, demonstrating that this approach can aid in planning future trials. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

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.

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.

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.

Apathy Is Related to Cognitive Control and Striatum Volumes in Prodromal Huntington's Disease.

OBJECTIVES: Apathy is a debilitating symptom of Huntington's disease (HD) and manifests before motor diagnosis, making it an excellent therapeutic target in the preclinical phase of Huntington's disease (prHD). HD is a neurological genetic disorder characterized by cognitive and motor impairment, and psychiatric abnormalities. Apathy is not well characterized within the prHD. In previous literature, damage to the caudate and putamen has been correlated with increased apathy in other neurodegenerative and movement disorders. The objective of this study was to determine whether apathy severity in individuals with prHD is related to striatum volumes and cognitive control. We hypothesized that, within prHD individuals, striatum volumes and cognitive control scores would be related to apathy. METHODS: We constructed linear mixed models to analyze striatum volumes and cognitive control, a composite measure that includes tasks assessing with apathy scores from 797 prHD participants. The outcome variable for each model was apathy, and the independent variables for the four separate models were caudate volume, putamen volume, cognitive control score, and motor symptom score. We also included depression as a covariate to ensure that our results were not solely related to mood. RESULTS: Caudate and putamen volumes, as well as measures of cognitive control, were significantly related to apathy scores even after controlling for depression. CONCLUSIONS: The behavioral apathy expressed by these individuals was related to regions of the brain commonly associated with isolated apathy, and not a direct result of mood symptoms. (JINS, 2019, 25, 462-469).

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.

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.

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.

Prefrontal cortex white matter tracts in prodromal Huntington disease.

Huntington disease (HD) is most widely known for its selective degeneration of striatal neurons but there is also growing evidence for white matter (WM) deterioration. The primary objective of this research was to conduct a large-scale analysis using multisite diffusion-weighted imaging (DWI) tractography data to quantify diffusivity properties along major prefrontal cortex WM tracts in prodromal HD. Fifteen international sites participating in the PREDICT-HD study collected imaging and neuropsychological data on gene-positive HD participants without a clinical diagnosis (i.e., prodromal) and gene-negative control participants. The anatomical prefrontal WM tracts of the corpus callosum (PFCC), anterior thalamic radiations (ATRs), inferior fronto-occipital fasciculi (IFO), and uncinate fasciculi (UNC) were identified using streamline tractography of DWI. Within each of these tracts, tensor scalars for fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity coefficients were calculated. We divided prodromal HD subjects into three CAG-age product (CAP) groups having Low, Medium, or High probabilities of onset indexed by genetic exposure. We observed significant differences in WM properties for each of the four anatomical tracts for the High CAP group in comparison to controls. Additionally, the Medium CAP group presented differences in the ATR and IFO in comparison to controls. Furthermore, WM alterations in the PFCC, ATR, and IFO showed robust associations with neuropsychological measures of executive functioning. These results suggest long-range tracts essential for cross-region information transfer show early vulnerability in HD and may explain cognitive problems often present in the prodromal stage. Hum Brain Mapp 36:3717-3732, 2015. © 2015 Wiley Periodicals, Inc.

Impact of autocorrelation on functional connectivity.

Although the impact of serial correlation (autocorrelation) in residuals of general linear models for fMRI time-series has been studied extensively, the effect of autocorrelation on functional connectivity studies has been largely neglected until recently. Some recent studies based on results from economics have questioned the conventional estimation of functional connectivity and argue that not correcting for autocorrelation in fMRI time-series results in "spurious" correlation coefficients. In this paper, first we assess the effect of autocorrelation on Pearson correlation coefficient through theoretical approximation and simulation. Then we present this effect on real fMRI data. To our knowledge this is the first work comprehensively investigating the effect of autocorrelation on functional connectivity estimates. Our results show that although FC values are altered, even following correction for autocorrelation, results of hypothesis testing on FC values remain very similar to those before correction. In real data we show this is true for main effects and also for group difference testing between healthy controls and schizophrenia patients. We further discuss model order selection in the context of autoregressive processes, effects of frequency filtering and propose a preprocessing pipeline for connectivity studies.

Diffusion weighted imaging of prefrontal cortex in prodromal Huntington's disease.

Huntington's disease (HD) is a devastating neurodegenerative disease with no effective disease-modifying treatments. There is considerable interest in finding reliable indicators of disease progression to judge the efficacy of novel treatments that slow or stop disease onset before debilitating signs appear. Diffusion-weighted imaging (DWI) may provide a reliable marker of disease progression by characterizing diffusivity changes in white matter (WM) in individuals with prodromal HD. The prefrontal cortex (PFC) may play a role in HD progression due to its prominent striatal connections and documented role in executive function. This study uses DWI to characterize diffusivity in specific regions of PFC WM defined by FreeSurfer in 53 prodromal HD participants and 34 controls. Prodromal HD individuals were separated into three CAG-Age Product (CAP) groups (16 low, 22 medium, 15 high) that indexed baseline progression. Statistically significant increases in mean diffusivity (MD) and radial diffusivity (RD) among CAP groups relative to controls were seen in inferior and lateral PFC regions. For MD and RD, differences among controls and HD participants tracked with baseline disease progression. The smallest difference was for the low group and the largest for the high group. Significant correlations between Trail Making Test B (TMTB) and mean fractional anisotropy (FA) and/or RD paralleled group differences in mean MD and/or RD in several right hemisphere regions. The gradient of effects that tracked with CAP group suggests DWI may provide markers of disease progression in future longitudinal studies as increasing diffusivity abnormalities in the lateral PFC of prodromal HD individuals.

Value-driven attentional capture in adolescence.

Adolescence has been characterized as a period of both opportunity and vulnerability. Numerous clinical conditions, including substance-use disorders, often emerge during adolescence. These maladaptive behaviors have been linked to problems with cognitive control, yet few studies have investigated how rewards differentially modulate attentional processes in adolescents versus adults. Here, we trained adults and adolescents on a visual task to establish stimulus-reward associations. Later, we assessed learning in an extinction task in which previously rewarded stimuli periodically appeared as distractors. Both age groups initially demonstrated value-driven attentional capture; however, the effect persisted longer in adolescents than in adults. The results could not be explained by developmental differences in visual working memory. Given the importance of attentional control to daily behaviors and clinical conditions such as attention-deficit/hyperactivity disorder, these results reveal that cognitive control failures in adolescence may be linked to a value-based attentional-capture effect.

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.

Tri-Clustering Dynamic Functional Network Connectivity Identifies Significant Schizophrenia Effects Across Multiple States in Distinct Subgroups of Individuals.

Background: Brain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). However, such an approach does not factor in the homogeneity of underlying data and may result in a less meaningful subgrouping of the data set. Methods: Dynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more "apples-to-apples" comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state. Results: Resulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared with HC subjects, SZ show hypoconnectivity (low positive) among subcortical, default mode, cognitive control, but hyperconnectivity (high positive) between sensory networks in most tri-clusters. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks than SZ. Results also provide a statistically significant difference in SZ and HC subject's reoccurrence time for two distinct dFNC states. Conclusions: Outcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in studying a heterogeneous disorder such as SZ and unconstrained experimental conditions as resting functional magnetic resonance imaging. Impact statement The current methods for analyzing dynamic functional network connectivity (dFNC) run k-means on a collection of dFNC windows, and each window includes all the pairs of independent component analysis networks. As such, it depicts a short-time connectivity pattern of the entire brain, and the k-means clusters fixed-length signatures that have an extent throughout the neural system. Consequently, there is a chance of missing connectivity signatures that span across a smaller subset of pairs. Dynamic-N-way tri-clustering further sorts the dFNC states by maximizing similarity across individuals, minimizing variance among the pairs of components within a state, and reporting more complex and transient patterns.

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.

Cognitive and daily functioning in older adults with vegetative symptoms of depression.

OBJECTIVES: In primary care 50-95% of patients with depression present with vegetative symptoms (VS). Based on the extant literature, older adults showing VS (but no dysphoria) may show functional impairment but this hypothesis has not been empirically tested. The goal of this study was to examine neurocognitive and daily functioning of elderly patients showing exclusively VS in comparison with patients presenting with VS and dysphoria. METHODS: Seven hundred and eighty-seven primary care patients received measures of neurocognition and daily functioning. Neurocognition was measured with the repeatable battery for the assessment of neuropsychological status (RBANS). Three groups were compared: (1) patients with two or more VS of depression without dysphoria (VS - D), (2) patients with at least one VS and dysphoria (VS + D), and (3) comparison patients without multiple VS or dysphoria. RESULTS: Nearly one third of the sample (31%) fell into the VS - D group, whereas 15% fell into the VS + D group. Both VS groups showed poorer neurocognition and activities of daily living than comparisons. Only one subtest of the RBANS (i.e., picture naming) showed a significant difference between VS + D and VS - D, and there was no significant difference on daily functioning. VS - D patients reported less frequent past history of depression and endorsed less anxiety compared to VS + D. CONCLUSIONS: Elderly patients presenting with clusters of VS with or without dysphoria show poorer neurocognitive and functional performance. Relative poorer cognition and daily functioning in VS - D are potential harbingers of further decline and consistent with under-reporting of sadness in older age.

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.