Cortical surface abnormalities are different depending on the stage of schizophrenia: A cross-sectional vertexwise mega-analysis of thickness, area and gyrification.

BACKGROUND: Brain magnetic resonance imaging studies have not investigated the cortical surface comprehensively in schizophrenia subjects by assessing thickness, surface area and gyrification separately during the first-episode of psychosis (FEP) or chronic schizophrenia (ChSch). METHODS: We investigated cortical surface abnormalities in 137 FEP patients and 240 ChSch subjects compared to 297 Healthy Controls (HC) contributed by five cohorts. Maps showing results of vertexwise between-group comparisons of cortical thickness, area, and gyrification were produced using T1-weighted datasets processed using FreeSurfer 5.3, followed by validated quality control protocols. RESULTS: FEP subjects showed large clusters of increased area and gyrification relative to HC in prefrontal and insuli cortices (Cohen's d: 0.049 to 0.28). These between-group differences occurred partially beyond the effect of sample. ChSch subjects displayed reduced cortical thickness relative to HC in smaller fronto-temporal foci (d: -0.73 to -0.35), but not beyond the effect of sample. Differences between FEP and HC subjects were associated with male gender, younger age, and earlier illness onset, while differences between ChSch and HC were associated with treatment-resistance and first-generation antipsychotic (FGA) intake independently of sample effect. CONCLUSIONS: Separate assessments of FEP and ChSch revealed abnormalities that differed in regional distribution, phenotypes affected and effect size. In FEP, associations of greater cortical area and gyrification abnormalities with earlier age of onset suggest an origin on anomalous neurodevelopment, while thickness reductions in ChSch are at least partially explained by treatment-resistance and FGA intake. Associations of between-group differences with clinical variables retained statistical significance beyond the effect of sample.

Gray Matter Volume in Elderly adults With ADHD: Associations of Symptoms and Comorbidities With Brain Structures.

Objective: To investigate total and selected region-of-interest-based gray matter volume (GMV) in older adults with ADHD. Method: Twenty-five elderly (≥65 years old) patients with ADHD and 34 healthy controls underwent 1.5-T magnetic resonance imaging (MRI). We used voxel-based morphometry to compare GMV between groups and performed a correlation analysis with ADHD symptoms and comorbidities. Results: Findings revealed a smaller total GMV in males with ADHD and a smaller GMV in the right medial frontal orbital area extending toward the medial frontal superior, the frontal superior, and the subgenual anterior cingulate cortex (ACC) besides correlations between inattentiveness and ACC (bilaterally) and left cerebellum, hyperactivity/impulsivity and the left frontal inferior orbital, depression and caudate (bilaterally), and the right inferior parietal lobule. Conclusion: Neural correlates in regions related to attention, executive control, and affective processing suggest that impairments in frontostriatal and frontoparietal-cerebellar areas observed in adults with ADHD persist into old age.

Fully Automated Habenula Segmentation Provides Robust and Reliable Volume Estimation Across Large Magnetic Resonance Imaging Datasets, Suggesting Intriguing Developmental Trajectories in Psychiatric Disease.

Studies of habenula (Hb) function and structure provided evidence of its involvement in psychiatric disorders, including schizophrenia and bipolar disorder. Previous studies using magnetic resonance imaging (manual/semiautomated segmentation) have reported conflicting results. Aiming to improve Hb segmentation reliability and the study of large datasets, we describe a fully automated protocol that was validated against manual segmentations and applied to 3 datasets (childhood/adolescence and adult bipolar disorder and schizophrenia). It achieved reliable Hb segmentation, providing robust volume estimations across a large age range and varying image acquisition parameters. Applying it to clinically relevant datasets, we found smaller Hb volumes in the adult bipolar disorder dataset and larger volumes in the adult schizophrenia dataset compared with healthy control subjects. There are indications that Hb volume in both groups shows deviating developmental trajectories early in life. This technique sets a precedent for future studies, as it allows for fast and reliable Hb segmentation and will be publicly available.

In vivo imaging evidence of poor cognitive resilience to Alzheimer's disease pathology in subjects with very low cognitive reserve from a low-middle income environment.

INTRODUCTION: Reduced cognitive reserve (CR) due to very low educational (VLE) levels may influence high dementia rates in low-middle income environments, leading to decreased cognitive resilience (RES) to Alzheimer´s disease (AD) pathology. However, in vivo findings in VLE groups confirming this prediction are lacking. METHODS: Cognitively impaired patients (with clinically defined AD dementia or amnestic mild cognitive impairment) and cognitively unimpaired older adults (n = 126) were recruited for a positron emission tomography (PET) and magnetic resonance imaging (MRI) investigation in Brazil, including 37 VLE individuals (≤5 years of education). A CR score was generated combining educational attainment and vocabulary knowledge. RES indices to AD pathology were calculated using standardized residuals from linear regression models relating current cognitive performance (episodic memory or overall cognition) to amyloid beta (Aß) burden Pittsburgh compound-B ([11C]PiB-PET). RESULTS: Aß burden was lower in VLE relative to highly-educated subjects (controlling for age, sex, and Mini-Mental Status Exam [MMSE] scores) in the overall cognitively impaired sample, and in dementia subjects when the three clinically defined groups were evaluated separately. In bivariate regression analyses for the overall sample, the RES index based on a composite cognitive score was predicted by CR, socioeconomic status, and hippocampal volume (but not white matter hyperintensities or intracranial volume [ICV]); in the multivariate model, only CR retained significance (and similar results were obtained in the Aß-positive subsample). In the multivariate model for the overall sample using the RES index based on memory performance, CR, hippocampal volume, and ICV were significant predictors, whereas only CR retained significance in Aß-positive subjects. DISCUSSION: Lower CR consistently predicted less resilience to AD pathology in older adults from a low-middle income environment.

Aerobic training modulates salience network and default mode network metabolism in subjects with mild cognitive impairment.

Aerobic training (AT) is a promising intervention to improve cognitive functioning. However, its modulatory effects on brain networks are not yet entirely understood. Sixty-five subjects with mild cognitive impairment performed a moderate intensity, 24-week AT program. Differences in resting regional brain glucose metabolism (rBGM) with FDG-PET were assessed before and after AT on a voxel-by-voxel basis. Structural equation modeling was used to create latent variables based on regions with significant rBGM changes and to test a hypothetical model about the inter-relationships between these changes. There were significant rBGM reductions in both anterior temporal lobes (ATL), left inferior frontal gyrus, left anterior cingulate cortex, right hippocampus, left meddle frontal gyrus and bilateral caudate nuclei. In contrast, there was an increase in rBGM in the right precuneus and left inferior frontal gyrus. Latent variables reflecting the salience network and ATL were created, while the precuneus represented the default mode network. In the model, salience network rBGM was decreased after AT. In contrast, rBGM in the default mode network increased as a final outcome. This result suggested improved salience network efficacy and increased control over other brain functional networks. The ATL network decreased its rBGM and connected to the salience network and default mode network with positive and negative correlations, respectively. The model fit values reached statistical significance, demonstrating that this model explained the variance in the measured data. In mild cognitive impairment subjects, AT modulated rBGM in salience network and default mode network nodes. Such changes were in the direction of the normally expected resting-state metabolic patterns of these networks.

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.

BACKGROUND: Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL). METHODS: We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients. RESULTS: Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei. CONCLUSION: The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.

Structural brain abnormalities in patients with type I bipolar disorder and suicidal behavior.

Some studies have identified brain morphological changes in the frontolimbic network (FLN) in bipolar subjects who attempt suicide (SA). The present study investigated neuroanatomical abnormalities in the FLN to find a possible neural signature for suicidal behavior in patients with bipolar disorder type I (BD-I). We used voxel-based morphometry to compare euthymic patients with BD-I who had attempted suicide (n=20), who had not attempted suicide (n=19) and healthy controls (HCs) (n=20). We also assessed the highest medical lethality of their previous SA. Compared to the participants who had not attempted suicide, the patients with BD-I who had attempted suicide exhibited significantly increased gray matter volume (GMV) in the right rostral anterior cingulate cortex (ACC), which was more pronounced and extended further to the left ACC in the high-lethality subgroup (p<0.05, with family-wise error (FWE) correction for multiple comparisons using small-volume correction). GMV in the insula and orbitofrontal cortex was also related to suicide lethality (p<0.05, FWE-corrected). The current findings suggest that morphological changes in the FLN could be a signature of previous etiopathogenic processes affecting regions related to suicidality and its severity in BD-I patients.

A voxel-based morphometry study of gray matter correlates of facial emotion recognition in bipolar disorder.

Facial emotion recognition (FER) is one of the many cognitive deficits reported in bipolar disorder (BD) patients. The aim of this study was to investigate neuroanatomical correlates of FER impairments in BD type I (BD-I). Participants comprised 21 euthymic BD-I patients without Axis I DSM IV-TR comorbidities and 21 healthy controls who were assessed using magnetic resonance imaging and the Penn Emotion Recognition Test (ER40). Preprocessing of images used DARTEL (diffeomorphic anatomical registration through exponentiated Lie algebra) for optimized voxel-based morphometry in SPM8. Compared with healthy subjects, BD-I patients performed poorly in on the ER40 and had reduced gray matter volume (GMV) in the left orbitofrontal cortex, superior portion of the temporal pole and insula. In the BD-I group, the statistical maps indicated a direct correlation between FER on the ER40 and right middle cingulate gyrus GMV. Our findings are consistent with the previous studies regarding the overlap of multiple brain networks of social cognition and BD neurobiology, particularly components of the anterior-limbic neural network.

Framingham Coronary Heart Disease Risk Score Can be Predicted from Structural Brain Images in Elderly Subjects.

Recent literature has presented evidence that cardiovascular risk factors (CVRF) play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer's disease (AD) and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies) in a sample of healthy elderly individuals. We aim to answer the following questions: is it possible to decode (i.e., to learn information regarding cardiovascular risk from structural brain images) enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: (i) we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease). (ii) When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. (iii) We found important gender differences, and the possible causes of that finding are discussed.

Cardiovascular risk in cognitively preserved elderlies is associated with glucose hypometabolism in the posterior cingulate cortex and precuneus regardless of brain atrophy and apolipoprotein gene variations.

Cardiovascular risk factors (CVRF) possibly contribute to the emergence of Alzheimer's disease (AD). Fluorodeoxyglucose-positron emission tomography (FDG-PET) has been widely used to demonstrate specific patterns of reduced cerebral metabolic rates of glucose (CMRgl) in subjects with AD and in non-demented carriers of the apolipoprotein ε4 (APOE ε4) allele, the major genetic risk factor for AD. However, functional neuroimaging studies investigating the impact of CVRF on cerebral metabolism have been scarce to date. The present FDG-PET study investigated 59 cognitively preserved elderlies divided into three groups according to their cardiovascular risk based on the Framingham 10-year risk Coronary Heart Disease Risk Profile (low-, medium-, and high-risk) to examine whether different levels of CVRF would be associated with reduced CMRgl, involving the same brain regions affected in early stages of AD. Functional imaging data were corrected for partial volume effects to avoid confounding effects due to regional brain atrophy, and all analyses included the presence of the APOE ε4 allele as a confounding covariate. Significant cerebral metabolism reductions were detected in the high-risk group when compared to the low-risk group in the left precuneus and posterior cingulate gyrus. This suggests that findings of brain hypometabolism similar to those seen in subjects with AD can be detected in association with the severity of cardiovascular risk in cognitively preserved individuals. Thus, a greater knowledge about how such factors influence brain functioning in healthy subjects over time may provide important insigths for the future development of strategies aimed at delaying or preventing the vascular-related triggering of pathologic brain changes in the AD.

Gray matter volumes in obsessive-compulsive disorder before and after fluoxetine or cognitive-behavior therapy: a randomized clinical trial.

Serotonin reuptake inhibitors and cognitive-behavior therapy (CBT) are considered first-line treatments for obsessive-compulsive disorder (OCD). However, little is known about their modulatory effects on regional brain morphology in OCD patients. We sought to document structural brain abnormalities in treatment-naive OCD patients and to determine the effects of pharmacological and cognitive-behavioral treatments on regional brain volumes. Treatment-naive patients with OCD (n=38) underwent structural magnetic resonance imaging scan before and after a 12-week randomized clinical trial with either fluoxetine or group CBT. Matched-healthy controls (n=36) were also scanned at baseline. Voxel-based morphometry was used to compare regional gray matter (GM) volumes of regions of interest (ROIs) placed in the orbitofrontal, anterior cingulate and temporolimbic cortices, striatum, and thalamus. Treatment-naive OCD patients presented smaller GM volume in the left putamen, bilateral medial orbitofrontal, and left anterior cingulate cortices than did controls (p<0.05, corrected for multiple comparisons). After treatment with either fluoxetine or CBT (n=26), GM volume abnormalities in the left putamen were no longer detectable relative to controls. ROI-based within-group comparisons revealed that GM volume in the left putamen significantly increased (p<0.012) in fluoxetine-treated patients (n=13), whereas no significant GM volume changes were observed in CBT-treated patients (n=13). This study supports the involvement of orbitofronto/cingulo-striatal loops in the pathophysiology of OCD and suggests that fluoxetine and CBT may have distinct neurobiological mechanisms of action.