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

PURPOSE/BACKGROUND: Hippocampal volume loss in early schizophrenia has been linked with markers of inflammation and oxidative stress, and with less response of negative symptoms. Aripiprazole has been reported to preserve hippocampal volume and to reduce inflammation. METHODS/PROCEDURES: Study 1 was a 12-month multicenter randomized placebo-controlled trial of citalopram added to clinician-determined second-generation antipsychotic medication in 95 patients with first-episode schizophrenia (FES), 19 of whom received aripiprazole. We compared participants taking aripiprazole with those on other antipsychotics to determine whether those on aripiprazole had less hippocampal volume loss. We also examined peripheral biomarker data from medication-naive patients with schizophrenia receiving 8 weeks of antipsychotic treatment (n = 24) to see whether markers of inflammation and oxidative stress that previously predicted hippocampal volume differed between aripiprazole (n = 9) and other antipsychotics (study 2). FINDINGS/RESULTS: Aripiprazole was associated with a mean increase in hippocampal volume of 0.35% (SD, 0.80%) compared with a 0.53% decrease (SD, 1.2%) with other antipsychotics during the first year of maintenance treatment in patients with FES. This difference was significant after adjusting for age, sex, citalopram treatment, and baseline Brief Psychiatric Rating Scale score (B = 0.0079, P = 0.03). Aripiprazole was also associated with reduced concentrations of the inflammatory cytokines interleukin-8 and tumor necrosis factor (P < 0.01) during the first 8 weeks of treatment in medication-naive patients with FES. IMPLICATIONS/CONCLUSIONS: These results suggest that aripiprazole may protect against hippocampal atrophy via an anti-inflammatory mechanism, but these results require replication in larger, randomized trials, and the clinical relevance of hippocampal volume loss is not established.

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

RATIONALE AND OBJECTIVES: Early prediction of incipient Alzheimer's disease (AD) dementia in individuals with mild cognitive impairment (MCI) is important for timely therapeutic intervention and identifying participants for clinical trials at greater risk of developing AD. Methods to predict incipient AD in MCI have mostly utilized cross-sectional data. Longitudinal data enables estimation of the rate of change of variables, which along with the variable levels have been shown to improve prediction power. While some efforts have already been made in this direction, all previous longitudinal studies have been based on observation periods longer than one year, hence limiting their practical utility. It remains to be seen if follow-up evaluations within shorter intervals can significantly improve the accuracy of prediction in this problem. Our aim was to determine the added value of incorporating 6-month longitudinal data for predicting progression from MCI to AD. MATERIALS AND METHODS: Using 6-months longitudinal data from 247 participants with MCI, we trained two Random Forest classifiers to distinguish between progressive MCI (n=162) and stable MCI (n=85) cases. These models utilized structural MRI, neurocognitive assessments, and demographic information. The first model (cross-sectional) only used baseline data. The second model (longitudinal) used data from both baseline and a 6-month follow-up evaluation allowing the model to additionally incorporate biomarkers' rate of change. RESULTS: The longitudinal model (AUC=0.87; accuracy=80.2%) performed significantly better (P<0.05) than the cross-sectional model (AUC=0.82; accuracy=71.7%). CONCLUSION: Short-term longitudinal assessments significantly enhance the performance of AD prediction models.

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

OBJECTIVE: For patients with medically intractable focal epilepsy, the benefit of epilepsy surgery must be weighed against the risk of cognitive decline. Clinical factors such as age and presurgical cognitive level partially predict cognitive outcome; yet, little is known about the role of cross-hemispheric white matter pathways in supporting postsurgical recovery of cognitive function. The purpose of this study is to determine whether the presurgical corpus callosum (CC) midsagittal area is associated with pre- to postsurgical change following epilepsy surgery. METHODS: In this observational study, we retrospectively identified 24 adult patients from an epilepsy resection series who completed preoperative high-resolution T1 -weighted magnetic resonance imaging (MRI) scans, as well as pre- and postsurgical neuropsychological testing. The total area and seven subregional areas of the CC were measured on the midsagittal MRI slice using an automated method. Standardized indices of auditory-verbal working memory and delayed memory were used to probe cognitive change from pre- to postsurgery. CC total and subregional areas were regressed on memory-change scores, after controlling for overall brain volume, age, presurgical memory scores, and duration of epilepsy. RESULTS: Patients had significantly reduced CC area relative to healthy controls. We found a positive relationship between CC area and change in working memory, but not delayed memory; specifically, the larger the CC, the greater the postsurgical improvement in working memory (ß = 0.523; p = 0.009). Effects were strongest in posterior CC subregions. There was no relationship between CC area and presurgical memory scores. SIGNIFICANCE: Findings indicate that larger CC area, measured presurgically, is related to improvement in working memory abilities following epilepsy surgery. This suggests that transcallosal pathways may play an important, yet little understood, role in postsurgical recovery of cognitive functions.

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

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

Log-Cholesky filtering of diffusion tensor fields: Impact on noise reduction.

Diffusion tensor imaging (DTI) is a powerful neuroimaging technique that provides valuable insights into the microstructure and connectivity of the brain. By measuring the diffusion of water molecules along neuronal fibers, DTI allows the visualization and study of intricate networks of neural pathways. DTI is a noise-sensitive method, where a low signal-to-noise ratio (SNR) results in significant errors in the estimated tensor field. Tensor field regularization is an effective solution for noise reduction. Diffusion tensors are represented by symmetric positive-definite (SPD) matrices. The space of SPD matrices may be viewed as a Riemannian manifold after defining a suitable metric on its tangent bundle. The Log-Cholesky metric is a recently developed concept with advantages over previously defined Riemannian metrics, such as the affine-invariant and Log-Euclidean metrics. The utility of the Log-Cholesky metric for tensor field regularization and noise reduction has not been investigated in detail. This manuscript provides a quantitative investigation of the impact of Log-Cholesky filtering on noise reduction in DTI. It also provides sufficient details of the linear algebra and abstract differential geometry concepts necessary to implement this technique as a simple and effective solution to filtering diffusion tensor fields.

Prediction of brain age in individuals with and at risk for alcohol use disorder using brain morphological features.

Brain age measures predicted from structural and functional brain features are increasingly being used to understand brain integrity, disorders, and health. While there is a vast literature showing aberrations in both structural and functional brain measures in individuals with and at risk for alcohol use disorder (AUD), few studies have investigated brain age in these groups. The current study examines brain age measures predicted using brain morphological features, such as cortical thickness and brain volume, in individuals with a lifetime diagnosis of AUD as well as in those at higher risk to develop AUD from families with multiple members affected with AUD (i.e., higher family history density (FHD) scores). The AUD dataset included a group of 30 adult males (mean age = 41.25 years) with a lifetime diagnosis of AUD and currently abstinent and a group of 30 male controls (mean age = 27.24 years) without any history of AUD. A second dataset of young adults who were categorized based on their FHD scores comprised a group of 40 individuals (20 males) with high FHD of AUD (mean age = 25.33 years) and a group of 31 individuals (18 males) with low FHD (mean age = 25.47 years). Brain age was predicted using 187 brain morphological features of cortical thickness and brain volume in an XGBoost regression model; a bias-correction procedure was applied to the predicted brain age. Results showed that both AUD and high FHD individuals showed an increase of 1.70 and 0.09 years (1.08 months), respectively, in their brain age relative to their chronological age, suggesting accelerated brain aging in AUD and risk for AUD. Increased brain age was associated with poor performance on neurocognitive tests of executive functioning in both AUD and high FHD individuals, indicating that brain age can also serve as a proxy for cognitive functioning and brain health. These findings on brain aging in these groups may have important implications for the prevention and treatment of AUD and ensuing cognitive decline.

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

OBJECTIVES: Schizophrenia and bipolar disorder may share common neurobiological mechanisms, but few studies have directly compared gray and white matter structure in these disorders. We used diffusion-weighted magnetic resonance imaging and a region of interest based analysis to identify overlapping and distinct gray and white matter abnormalities in 35 patients with schizophrenia and 20 patients with bipolar I disorder in comparison to 56 healthy volunteers. METHODS: We examined fractional anisotropy within the white matter and mean diffusivity within the gray matter in 42 regions of interest defined on a probabilistic atlas following non-linear registration of the images to atlas space. RESULTS: Patients with schizophrenia had significantly lower fractional anisotropy in temporal (superior temporal and parahippocampal) and occipital (superior and middle occipital) white matter compared to patients with bipolar disorder and healthy volunteers. By contrast, both patient groups demonstrated significantly higher mean diffusivity in frontal (inferior frontal and lateral orbitofrontal) and temporal (superior temporal and parahippocampal) gray matter compared to healthy volunteers, but did not differ from each other. CONCLUSIONS: Our study implicates overlapping gray matter frontal and temporal lobe structural alterations in the neurobiology of schizophrenia and bipolar I disorder, but suggests that temporal and occipital lobe white matter deficits may be an additional risk factor for schizophrenia. Our findings may have relevance for future diagnostic classification systems and the identification of susceptibility genes for these disorders.

Relationship between suicidality and impulsivity in bipolar I disorder: a diffusion tensor imaging study.

BACKGROUND: Impulsivity is characteristic of individuals with bipolar disorder and may be a contributing factor to the high rate of suicide in patients with this disorder. Although white matter abnormalities have been implicated in the pathophysiology of bipolar disorder, their relationship to impulsivity and suicidality in this disorder has not been well-investigated. METHODS: Diffusion tensor imaging scans were acquired in 14 bipolar disorder patients with a prior suicide attempt, 15 bipolar disorder patients with no prior suicide attempt, and 15 healthy volunteers. Bipolar disorder patients received clinical assessments including measures of impulsivity, depression, mania, and anxiety. Images were processed using the Tract-Based Spatial Statistics method in the FSL software package. RESULTS: Bipolar disorder patients with a prior suicide attempt had lower fractional anisotropy (FA) within the left orbital frontal white matter (p < 0.05, corrected) and higher overall impulsivity compared to patients without a previous suicide attempt. Among patients with a prior suicide attempt, FA in the orbital frontal white matter region correlated inversely with motor impulsivity. CONCLUSIONS: Abnormal orbital frontal white matter may play a role in impulsive and suicidal behavior among patients with bipolar disorder.

A new approach to symmetric registration of longitudinal structural MRI of the human brain.

BACKGROUND: This paper presents the Automatic Temporal Registration Algorithm (ATRA) for symmetric rigid-body registration of longitudinal T1-weighted three-dimensional MRI scans of the human brain. This is a fundamental processing step in computational neuroimaging. NEW METHOD: The notion of leave-one-out consistent (LOOC) landmarks with respect to a supervised landmark detection algorithm is introduced. An automatic algorithm is presented for identification of LOOC landmarks on MRI scans. Multiple sets of LOOC landmarks are identified on each volume and a Generalized Orthogonal Procrustes Analysis of the landmarks is used to find a rigid-body transformation of each volume into a common space where the volumes are aligned precisely. RESULTS: Qualitative and quantitative evaluations of ATRA registration accuracy were performed using 2012 volumes from 503 subjects (4 longitudinal volumes/subject), and on a further 120 volumes acquired from 3 normal subjects (40 longitudinal volumes/subject). Since the ground truth registrations are unknown, we devised a novel method for showing that ATRA's registration accuracy is at least better than 0.5 mm translation or 0.5° rotation. COMPARISON WITH EXISTING METHOD(S): In comparison with existing methods, ATRA does not require any image preprocessing (e.g., skull-stripping or intensity normalization) and can handle conditions where rigid-body motion assumptions are not true (e.g., movement in eyes, jaw, neck) and brain tissue loss over time in neurodegenerative diseases. In a systematic comparison with the FSL FLIRT algorithm, ATRA provided faster and more accurate registrations. CONCLUSIONS: The algorithm is symmetric, in the sense that any permutation of the input volumes does not change the transformation matrices, and unbiased, in that all volumes undergo exactly one interpolation operation, which precisely aligns them in a common space. There is no interpolation bias and no reference volume. All volumes are treated exactly the same. The algorithm is fast and highly accurate.

Survival Analysis in Cognitively Normal Subjects and in Patients with Mild Cognitive Impairment Using a Proportional Hazards Model with Extreme Gradient Boosting Regression.

BACKGROUND: Evaluating the risk of Alzheimer's disease (AD) in cognitively normal (CN) and patients with mild cognitive impairment (MCI) is extremely important. While MCI-to-AD progression risk has been studied extensively, few studies estimate CN-to-MCI conversion risk. The Cox proportional hazards (PH), a widely used survival analysis model, assumes a linear predictor-risk relationship. Generalizing the PH model to more complex predictor-risk relationships may increase risk estimation accuracy. OBJECTIVE: The aim of this study was to develop a PH model using an Xgboost regressor, based on demographic, genetic, neuropsychiatric, and neuroimaging predictors to estimate risk of AD in patients with MCI, and the risk of MCI in CN subjects. METHODS: We replaced the Cox PH linear model with an Xgboost regressor to capture complex interactions between predictors, and non-linear predictor-risk associations. We endeavored to limit model inputs to noninvasive and more widely available predictors in order to facilitate future applicability in a wider setting. RESULTS: In MCI-to-AD (n = 882), the Xgboost model achieved a concordance index (C-index) of 84.5%. When the model was used for MCI risk prediction in CN (n = 100) individuals, the C-index was 73.3%. In both applications, the C-index was statistically significantly higher in the Xgboost in comparison to the Cox PH model. CONCLUSION: Using non-linear regressors such as Xgboost improves AD dementia risk assessment in CN and MCI. It is possible to achieve reasonable risk stratification using predictors that are relatively low-cost in terms of time, invasiveness, and availability. Future strategies for improving AD dementia risk estimation are discussed.

Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures.

Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study aimed to identify specific features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random forests (RF) classification model. Features included fMRI-based resting-state functional connectivity (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individuals without a history of AUD. It was found that the RF model achieved a classification accuracy of 86.67% (AUC = 93%) and identified key features of FC and impulsivity that significantly contributed to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested significant differences in impulsivity and alterations in functional connectivity relative to controls. Specifically, AUD showed heightened impulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three connections involving six regions. Relative to controls, visuo-spatial short-term working memory was also found to be impaired in AUD. In conclusion, specific multidomain features of brain connectivity, impulsivity, and neuropsychological performance can be used in a machine learning framework to effectively classify AUD individuals from healthy controls.

Platelet Function Is Associated With Dementia Risk in the Framingham Heart Study.

Background Vascular function is compromised in Alzheimer disease (AD) years before amyloid and tau pathology are detected and a substantial body of work shows abnormal platelet activation states in patients with AD. The aim of our study was to investigate whether platelet function in middle age is independently associated with future risk of AD. Methods and Results We examined associations of baseline platelet function with incident dementia risk in the community-based FHS (Framingham Heart Study) longitudinal cohorts. The association between platelet function and risk of dementia was evaluated using the cumulative incidence function and inverse probability weighted Cox proportional cause-specific hazards regression models, with adjustment for demographic and clinical covariates. Platelet aggregation response was measured by light transmission aggregometry. The final study sample included 1847 FHS participants (average age, 53.0 years; 57.5% women). During follow-up (median, 20.5 years), we observed 154 cases of incident dementia, of which 121 were AD cases. Results from weighted models indicated that platelet aggregation response to adenosine diphosphate 1.0 µmol/L was independently and positively associated with dementia risk, and it was preceded in importance only by age and hypertension. Sensitivity analyses showed associations with the same directionality for participants defined as adenosine diphosphate hyper-responders, as well as the platelet response to 0.1 µmol/L epinephrine. Conclusions Our study shows individuals free of antiplatelet therapy with a higher platelet response are at higher risk of dementia in late life during a 20-year follow-up, reinforcing the role of platelet function in AD risk. This suggests that platelet phenotypes may be associated with the rate of dementia and potentially have prognostic value.

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

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

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

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

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

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

The Neutrophil to Lymphocyte Ratio Is Associated With the Risk of Subsequent Dementia in the Framingham Heart Study.

Objective: Active neutrophils are important contributors to Alzheimer's disease (AD) pathology through the formation of capillary stalls that compromise cerebral blood flow (CBF) and through aberrant neutrophil signaling that advances disease progression. The neutrophil to lymphocyte ratio (NLR) is a proxy of neutrophil-mediated inflammation, and higher NLR is found in persons diagnosed with clinical AD. The objective of this study was to investigate whether increased NLR in older adults is independently associated with the risk of subsequent dementia. Methods: We examined associations of baseline NLR with incident dementia risk in the community-based Framingham Heart Study (FHS) longitudinal cohorts. The association between NLR and risk of dementia was evaluated using the cumulative incidence function (CIF) and inverse probability-weighted Cox proportional cause-specific hazards regression models, with adjustment for age, sex, body mass index (BMI), systolic and diastolic blood pressure, diabetes, current smoking status, low-density lipoprotein (LDH), high-density lipoprotein (LDL), total cholesterol, triglycerides, and history of cardiovascular disease (CVD). Random forest survival models were used to evaluate the relative predictive value of the model covariates on dementia risk. Results: The final study sample included 1,648 participants with FHS (average age, 69 years; 56% women). During follow-up (median, 5.9 years), we observed 51 cases of incident dementia, of which 41 were AD cases. Results from weighted models suggested that the NLR was independently associated with incident dementia, and it was preceded in predictive value only by age, history of CVD, and blood pressure at baseline. Conclusion: Our study shows that individuals with higher NLR are at a greater risk of subsequent dementia during a 5.9-year follow-up period. Further evaluating the role of neutrophil-mediated inflammation in AD progression may be warranted.

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

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

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

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

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

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

Air pollution and hippocampal atrophy in first episode schizophrenia.

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