Grey Matter Volume Patterns in Thalamic Nuclei are Associated with Schizotypy in Healthy Subjects

IntroductionSchizotypy refers to a set of temporally stable traits that are observed in the general population and that resemble, in attenuated form, the symptoms of schizophrenia. In a previous work, we identified volumetric patterns in thalamic subregions which were associated with disease status, and trained a random forests classifier, accounting for such thalamic volumetric patterns, that discriminated healthy controls (HC) from patients with schizophrenia (SCZ) (81% accuracy) [1].Objectivesi) to assess performance of random forests classifier developed by Pergola and coworkers [1], in an independent sample of healthy subjects; ii) to test whether false positives (FP), i.e. HC classified as SCZ based on such classifier would be associated with greater schizotypy compared with true negatives (TN), i.e. HC classified as such.MethodsA total of 167 HC participated in the MRI study and filled the Schizotypal Personality Questionnaire (SPQ). We pre-processed MRI data with SPM8 and DARTEL. Then, we used thalamic grey matter volumes (GMV) as features in the random forests prediction of disease status at the single subject level. Finally, we tested SPQ scores differences between FP and TN with Mann-Whitney test.ResultsThe classification accuracy was 71%. FP had greater SPQ scores compared to TN (P = 0.007).ConclusionsClassification accuracy of our classifier in an independent sample suggests that thalamic GMV patterns are reproducible markers of disease status. Furthermore, the present results also suggest that variability of thalamic GMV patterns in HC may have relevance for subclinical phenotypes related to schizophrenia spectrum.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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Grey matter volume patterns in thalamic nuclei are associated with familial risk for schizophrenia

Schizophrenia Research ◽

10.1016/j.schres.2016.07.005 ◽

2017 ◽

Vol 180 ◽

pp. 13-20 ◽

Cited By ~ 24

Author(s):

Giulio Pergola ◽

Silvestro Trizio ◽

Pasquale Di Carlo ◽

Paolo Taurisano ◽

Marina Mancini ◽

...

Keyword(s):

Grey Matter ◽

Familial Risk ◽

Grey Matter Volume ◽

Thalamic Nuclei ◽

Matter Volume

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Correlation of reduced social communicational and interactional skills with regional grey matter volumes in schizophrenia patients

Acta Neuropsychiatrica ◽

10.1017/neu.2017.9 ◽

2017 ◽

Vol 29 (6) ◽

pp. 374-381 ◽

Cited By ~ 2

Author(s):

Miho Ota ◽

Junko Matsuo ◽

Noriko Sato ◽

Toshiya Teraishi ◽

Hiroaki Hori ◽

...

Keyword(s):

Healthy Subjects ◽

Grey Matter ◽

Cingulate Cortex ◽

Autism Spectrum ◽

Anterior Cingulate ◽

Grey Matter Volume ◽

Temporal Region ◽

Matter Volume ◽

Left Posterior ◽

The Social

ObjectiveRecent studies have detected similarities between autism spectrum disorder and schizophrenia. We investigated structural abnormalities associated with autistic-like traits in patients with schizophrenia by voxel-based morphometry.MethodsPatients with schizophrenia and healthy subjects were evaluated by the adult version of the social responsiveness scale (SRS-A), which is sensitive to autistic traits and symptoms even under subthreshold conditions, and magnetic resonance imaging.ResultsThere were significant decreases in the anterior cingulate cortex, bilateral hippocampi, cerebellums, and right insula of patients with schizophrenia, compared with healthy subjects. We found significant negative correlations of the social communication and interaction (SCI) score, a subscale of SRS-A, with grey matter volume in the left posterior superior temporal region of schizophrenia patients. When subscales of SCI were examined separately in schizophrenic patients, negative correlations were observed between the social cognition score and the volumes of the left posterior superior temporal region, and between social motivation and the posterior cingulate cortex.ConclusionWe found significant negative correlation between the SCI score and the grey matter volume in the left posterior superior temporal region of schizophrenia patients. This area was the region affected in previous studies of autistic spectrum disorders. Further, this area was associated with the theory of mind. Schizophrenia patients not necessarily show the impairment of SCI, nor this correlated region was not always the point with schizophrenia-specific change. However, we reveal the relationship between the left posterior superior temporal gyrus and the severity of the SCI in schizophrenia by using with SRS-A.

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Central nervous system grey matter decreases in volume in smokers impacting cognitive abilities: A systematic review

European Psychiatry ◽

10.1016/j.eurpsy.2017.01.1784 ◽

2017 ◽

Vol 41 (S1) ◽

pp. s883-s883 ◽

Cited By ~ 1

Author(s):

M. Vnukova ◽

R. Ptacek ◽

J. Raboch ◽

G. Stefano

Keyword(s):

Risk Factors ◽

Cognitive Abilities ◽

Emotional Regulation ◽

Grey Matter ◽

Group Differences ◽

Grey Matter Volume ◽

Gender Specific Differences ◽

Brain Changes ◽

Competing Interest ◽

Brain Processing

BackgroundEven though cigarette smoking is a leading cause of preventable mortality, worldwide tobacco is consumed by approximately 22% of population. Smoking is also one of the risk factors for cardiovascular disease and it impacts our brain processing as well as being one of the recognised risk factors for Alzheimer's disease. The tobacco toxins may cause these disorders, e.g., nicotine at high levels, which are inhaled, resulting in preclinical brain changes. Researchers suggest that there are differences in brain volume between smokers and non-smokers. This review examines these differences on the brain grey matter volume (GMV).Material/methodsIn March/April 2015: MedLine, Embasse and PsycInfo were searched using terms: “grey matter”, “voxel based”, “smoking” and “cigarette”.ResultsStudies found brain GMV decreases in smokers compared to non-smokers. Furthermore, gender specific differences were found, while thalamus and cerebellum was affected in both genders decrease in olfactory gyrus was found only in male smokers. Age group differences were also found and these may suggest pre-existing abnormalities that lead to nicotine dependence in younger individuals. Only one study found positive correlation between number of pack-years and GMV.ConclusionSmoking decreases the volume of grey matter in most brain areas. This decrease may be responsible for the cognitive impairment and difficulties with emotional regulation in smokers compared with non-smokers.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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The relationship of symptom dimensions and grey matter volume in a transdiagnostic cohort: Results from the DFG FOR2107

10.1055/s-0039-1679161 ◽

2019 ◽

Author(s):

F Stein ◽

G Lemmer ◽

S Schmitt ◽

K Brosch ◽

E Fischer ◽

...

Keyword(s):

Grey Matter ◽

Grey Matter Volume ◽

Symptom Dimensions ◽

Matter Volume ◽

Relationship Of ◽

The Relationship

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Social cognition and brain organization in chimpanzees (Pan troglodytes) and bonobos (Pan paniscus)

10.1093/oso/9780198728511.003.0014 ◽

2018 ◽

Author(s):

William D. Hopkins ◽

Cheryl D. Stimpson ◽

Chet C. Sherwood

Keyword(s):

Social Cognition ◽

Social Behaviour ◽

Grey Matter ◽

Species Differences ◽

Pan Paniscus ◽

Grey Matter Volume ◽

Evolutionary Models ◽

Brain Organization ◽

Matter Volume ◽

Cognition Sociale

Bonobos and chimpanzees are two closely relates species of the genus Pan, yet they exhibit marked differences in anatomy, behaviour and cognition. For this reason, comparative studies on social behaviour, cognition and brain organization between these two species provide important insights into evolutionary models of human origins. This chapter summarizes studies on socio-communicative competencies and social cognition in chimpanzees and bonobos from the authors’ laboratory in comparison to previous reports. Additionally, recent data on species differences and similarities in brain organization in grey matter volume and distribution is presented. Some preliminary findings on microstructural brain organization such as neuropil space and cellular distribution in key neurotransmitters and neuropeptides involved in social behaviour and cognition is presented. Though these studies are in their infancy, the findings point to potentially important differences in brain organization that may underlie bonobo and chimpanzees’ differences in social behaviour, communication and cognition. Les bonobos et les chimpanzés sont deux espèces du genus Pan prochement liées, néanmoins ils montrent des différences anatomiques, comportementales et cognitives marquées. Pour cette raison, les études comparatives sur le comportement social, la cognition et l’organisation corticale entre ces deux espèces fournissent des idées sur les modèles évolutionnaires des origines humaines. Dans ce chapitre, nous résumons des études sur les compétences socio-communicatives et la cognition sociale chez les chimpanzés et les bonobos de notre laboratoire en comparaison avec des rapports précédents. En plus, nous présentons des données récentes sur les différences et similarités d’organisation corticale du volume et distribution de la matière grise entre espèces. Nous présentons plus de résultats préliminaires sur l’organisation corticale microstructurale comme l’espace neuropile et la division cellulaire dans des neurotransmetteurs clés et les neuropeptides impliqués dans le comportement social et la cognition. Bien que ces études sont dans leur enfance, les résultats montrent des différences d’organisation corticale importantes qui sont à la base des différences de comportement social, la communication et la cognition entre les bonobos et les chimpanzés.

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Modelling the cascade of biomarker changes in GRN-related frontotemporal dementia

Journal of Neurology Neurosurgery & Psychiatry ◽

10.1136/jnnp-2020-323541 ◽

2021 ◽

pp. jnnp-2020-323541

Author(s):

Jessica L Panman ◽

Vikram Venkatraghavan ◽

Emma L van der Ende ◽

Rebecca M E Steketee ◽

Lize C Jiskoot ◽

...

Keyword(s):

White Matter ◽

Frontotemporal Dementia ◽

Disease Severity ◽

Grey Matter ◽

Clinical Stage ◽

Data Driven ◽

Grey Matter Volume ◽

Matter Volume ◽

Mutation Carriers ◽

Individual Disease

ObjectiveProgranulin-related frontotemporal dementia (FTD-GRN) is a fast progressive disease. Modelling the cascade of multimodal biomarker changes aids in understanding the aetiology of this disease and enables monitoring of individual mutation carriers. In this cross-sectional study, we estimated the temporal cascade of biomarker changes for FTD-GRN, in a data-driven way.MethodsWe included 56 presymptomatic and 35 symptomatic GRN mutation carriers, and 35 healthy non-carriers. Selected biomarkers were neurofilament light chain (NfL), grey matter volume, white matter microstructure and cognitive domains. We used discriminative event-based modelling to infer the cascade of biomarker changes in FTD-GRN and estimated individual disease severity through cross-validation. We derived the biomarker cascades in non-fluent variant primary progressive aphasia (nfvPPA) and behavioural variant FTD (bvFTD) to understand the differences between these phenotypes.ResultsLanguage functioning and NfL were the earliest abnormal biomarkers in FTD-GRN. White matter tracts were affected before grey matter volume, and the left hemisphere degenerated before the right. Based on individual disease severities, presymptomatic carriers could be delineated from symptomatic carriers with a sensitivity of 100% and specificity of 96.1%. The estimated disease severity strongly correlated with functional severity in nfvPPA, but not in bvFTD. In addition, the biomarker cascade in bvFTD showed more uncertainty than nfvPPA.ConclusionDegeneration of axons and language deficits are indicated to be the earliest biomarkers in FTD-GRN, with bvFTD being more heterogeneous in disease progression than nfvPPA. Our data-driven model could help identify presymptomatic GRN mutation carriers at risk of conversion to the clinical stage.

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Clinical and anatomical heterogeneity in autistic spectrum disorder: a structural MRI study

Psychological Medicine ◽

10.1017/s0033291709991541 ◽

2009 ◽

Vol 40 (7) ◽

pp. 1171-1181 ◽

Cited By ~ 70

Author(s):

F. Toal ◽

E. M. Daly ◽

L. Page ◽

Q. Deeley ◽

B. Hallahan ◽

...

Keyword(s):

Asperger Syndrome ◽

Autistic Spectrum Disorder ◽

Grey Matter ◽

Clinical Phenotype ◽

Brain Regions ◽

Spectrum Disorder ◽

Grey Matter Volume ◽

Brain Anatomy ◽

Autistic Spectrum ◽

Adults With Asd

BackgroundAutistic spectrum disorder (ASD) is characterized by stereotyped/obsessional behaviours and social and communicative deficits. However, there is significant variability in the clinical phenotype; for example, people with autism exhibit language delay whereas those with Asperger syndrome do not. It remains unclear whether localized differences in brain anatomy are associated with variation in the clinical phenotype.MethodWe used voxel-based morphometry (VBM) to investigate brain anatomy in adults with ASD. We included 65 adults diagnosed with ASD (39 with Asperger syndrome and 26 with autism) and 33 controls who did not differ significantly in age or gender.ResultsVBM revealed that subjects with ASD had a significant reduction in grey-matter volume of medial temporal, fusiform and cerebellar regions, and in white matter of the brainstem and cerebellar regions. Furthermore, within the subjects with ASD, brain anatomy varied with clinical phenotype. Those with autism demonstrated an increase in grey matter in frontal and temporal lobe regions that was not present in those with Asperger syndrome.ConclusionsAdults with ASD have significant differences from controls in the anatomy of brain regions implicated in behaviours characterizing the disorder, and this differs according to clinical subtype.

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EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

Brain Sciences ◽

10.3390/brainsci9110326 ◽

2019 ◽

Vol 9 (11) ◽

pp. 326 ◽

Cited By ~ 5

Author(s):

Hong Zeng ◽

Zhenhua Wu ◽

Jiaming Zhang ◽

Chen Yang ◽

Hua Zhang ◽

...

Keyword(s):

Deep Learning ◽

Speaker Recognition ◽

Classification Accuracy ◽

Deep Neural Network ◽

Signal To Noise Ratio ◽

Single Subject ◽

Emotion Classification ◽

Eeg Signals ◽

Electroencephalogram Eeg ◽

Deep Learning Model

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

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Sex differences in neural correlates of common psychopathological symptoms in early adolescence

Psychological Medicine ◽

10.1017/s0033291720005140 ◽

2021 ◽

pp. 1-11

Author(s):

Francesca Biondo ◽

Charlotte Nymberg Thunell ◽

Bing Xu ◽

Congying Chu ◽

Tianye Jia ◽

...

Keyword(s):

Internalizing Symptoms ◽

Grey Matter ◽

Neural Correlates ◽

Brain Regions ◽

Female Adolescents ◽

Grey Matter Volume ◽

Community Based ◽

Psychopathological Symptom ◽

Strengths And Difficulties ◽

The Brain

Abstract Background Sex-related differences in psychopathology are known phenomena, with externalizing and internalizing symptoms typically more common in boys and girls, respectively. However, the neural correlates of these sex-by-psychopathology interactions are underinvestigated, particularly in adolescence. Methods Participants were 14 years of age and part of the IMAGEN study, a large (N = 1526) community-based sample. To test for sex-by-psychopathology interactions in structural grey matter volume (GMV), we used whole-brain, voxel-wise neuroimaging analyses based on robust non-parametric methods. Psychopathological symptom data were derived from the Strengths and Difficulties Questionnaire (SDQ). Results We found a sex-by-hyperactivity/inattention interaction in four brain clusters: right temporoparietal-opercular region (p < 0.01, Cohen's d = −0.24), bilateral anterior and mid-cingulum (p < 0.05, Cohen's d = −0.18), right cerebellum and fusiform (p < 0.05, Cohen's d = −0.20) and left frontal superior and middle gyri (p < 0.05, Cohen's d = −0.26). Higher symptoms of hyperactivity/inattention were associated with lower GMV in all four brain clusters in boys, and with higher GMV in the temporoparietal-opercular and cerebellar-fusiform clusters in girls. Conclusions Using a large, sex-balanced and community-based sample, our study lends support to the idea that externalizing symptoms of hyperactivity/inattention may be associated with different neural structures in male and female adolescents. The brain regions we report have been associated with a myriad of important cognitive functions, in particular, attention, cognitive and motor control, and timing, that are potentially relevant to understand the behavioural manifestations of hyperactive and inattentive symptoms. This study highlights the importance of considering sex in our efforts to uncover mechanisms underlying psychopathology during adolescence.

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