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Wang LL, Lui SS, Chan RC. Neuropsychology and Neurobiology of Negative Schizotypy: A Selective Review. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100317. [PMID: 38711865 PMCID: PMC11070600 DOI: 10.1016/j.bpsgos.2024.100317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/09/2024] [Accepted: 04/01/2024] [Indexed: 05/08/2024] Open
Abstract
Schizotypy refers to a latent personality organization that reflects liability to schizophrenia. Because schizotypy is a multidimensional construct, people with schizotypy vary in behavioral and neurobiological features. In this article, we selectively review the neuropsychological and neurobiological profiles of people with schizotypy, with a focus on negative schizotypy. Empirical evidence is presented for alterations of neuropsychological performance in negative schizotypy. We also cover the Research Domain Criteria domains of positive valence, social process, and sensorimotor systems. Moreover, we systematically summarize the neurobiological correlates of negative schizotypy at the structural, resting-state, and task-based neural levels, as well as the neurochemical level. The convergence and inconsistency of the evidence are critically reviewed. Regarding theoretical and clinical implications, we argue that negative schizotypy represents a useful organizational framework for studying neuropsychology and neurobiology across different psychiatric disorders.
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Affiliation(s)
- Ling-ling Wang
- School of Psychology, Shanghai Normal University, Shanghai, China
| | - Simon S.Y. Lui
- Department of Psychiatry, School of Clinical Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Raymond C.K. Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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Zhou Y, Pat N, Neale MC. Associations between resting state functional brain connectivity and childhood anhedonia: A reproduction and replication study. PLoS One 2023; 18:e0277158. [PMID: 37141274 PMCID: PMC10159190 DOI: 10.1371/journal.pone.0277158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/28/2023] [Indexed: 05/05/2023] Open
Abstract
BACKGROUND Previously, a study using a sample of the Adolescent Brain Cognitive Development (ABCD)® study from the earlier 1.0 release found differences in several resting state functional MRI (rsfMRI) brain connectivity measures associated with children reporting anhedonia. Here, we aim to reproduce, replicate, and extend the previous findings using data from the later ABCD study 4.0 release, which includes a significantly larger sample. METHODS To reproduce and replicate the previous authors' findings, we analyzed data from the ABCD 1.0 release (n = 2437), from an independent subsample from the newer ABCD 4.0 release (excluding individuals from the 1.0 release) (n = 6456), and from the full ABCD 4.0 release sample (n = 8866). Additionally, we assessed whether using a multiple linear regression approach could improve replicability by controlling for the effects of comorbid psychiatric conditions and sociodemographic covariates. RESULTS While the previously reported associations were reproducible, effect sizes for most rsfMRI measures were drastically reduced in replication analyses (including for both t-tests and multiple linear regressions) using the ABCD 4.0 (excluding 1.0) sample. However, 2 new rsfMRI measures (the Auditory vs. Right Putamen and the Retrosplenial-Temporal vs. Right-Thalamus-Proper measures) exhibited replicable associations with anhedonia and stable, albeit small, effect sizes across the ABCD samples, even after accounting for sociodemographic covariates and comorbid psychiatric conditions using a multiple linear regression approach. CONCLUSION The most statistically significant associations between anhedonia and rsfMRI connectivity measures found in the ABCD 1.0 sample tended to be non-replicable and inflated. Contrastingly, replicable associations exhibited smaller effects with less statistical significance in the ABCD 1.0 sample. Multiple linear regressions helped assess the specificity of these findings and control the effects of confounding covariates.
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Affiliation(s)
- Yi Zhou
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States of America
| | - Narun Pat
- Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Michael C. Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States of America
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Olsen AS, Høegh RMT, Hinrich JL, Madsen KH, Mørup M. Combining electro- and magnetoencephalography data using directional archetypal analysis. Front Neurosci 2022; 16:911034. [PMID: 35968377 PMCID: PMC9374169 DOI: 10.3389/fnins.2022.911034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/11/2022] [Indexed: 11/20/2022] Open
Abstract
Metastable microstates in electro- and magnetoencephalographic (EEG and MEG) measurements are usually determined using modified k-means accounting for polarity invariant states. However, hard state assignment approaches assume that the brain traverses microstates in a discrete rather than continuous fashion. We present multimodal, multisubject directional archetypal analysis as a scale and polarity invariant extension to archetypal analysis using a loss function based on the Watson distribution. With this method, EEG/MEG microstates are modeled using subject- and modality-specific archetypes that are representative, distinct topographic maps between which the brain continuously traverses. Archetypes are specified as convex combinations of unit norm input data based on a shared generator matrix, thus assuming that the timing of neural responses to stimuli is consistent across subjects and modalities. The input data is reconstructed as convex combinations of archetypes using a subject- and modality-specific continuous archetypal mixing matrix. We showcase the model on synthetic data and an openly available face perception event-related potential data set with concurrently recorded EEG and MEG. In synthetic and unimodal experiments, we compare our model to conventional Euclidean multisubject archetypal analysis. We also contrast our model to a directional clustering model with discrete state assignments to highlight the advantages of modeling state trajectories rather than hard assignments. We find that our approach successfully models scale and polarity invariant data, such as microstates, accounting for intersubject and intermodal variability. The model is readily extendable to other modalities ensuring component correspondence while elucidating spatiotemporal signal variability.
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Affiliation(s)
- Anders S. Olsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Rasmus M. T. Høegh
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- WS Audiology, Lynge, Denmark
| | - Jesper L. Hinrich
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Kristoffer H. Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark
| | - Morten Mørup
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
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Morante M, Kopsinis Y, Chatzichristos C, Protopapas A, Theodoridis S. Enhanced design matrix for task-related fMRI data analysis. Neuroimage 2021; 245:118719. [PMID: 34775007 DOI: 10.1016/j.neuroimage.2021.118719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/20/2021] [Accepted: 11/09/2021] [Indexed: 10/19/2022] Open
Abstract
In this paper, we introduce a novel methodology for the analysis of task-related fMRI data. In particular, we propose an alternative way for constructing the design matrix, based on the newly suggested Information-Assisted Dictionary Learning (IADL) method. This technique offers an enhanced potential, within the conventional GLM framework, (a) to efficiently cope with uncertainties in the modeling of the hemodynamic response function, (b) to accommodate unmodeled brain-induced sources, beyond the task-related ones, as well as potential interfering scanner-induced artifacts, uncorrected head-motion residuals and other unmodeled physiological signals, and (c) to integrate external knowledge regarding the natural sparsity of the brain activity that is associated with both the experimental design and brain atlases. The capabilities of the proposed methodology are evaluated via a realistic synthetic fMRI-like dataset, and demonstrated using a test case of a challenging fMRI study, which verifies that the proposed approach produces substantially more consistent results compared to the standard design matrix method. A toolbox extension for SPM is also provided, to facilitate the use and reproducibility of the proposed methodology.
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Affiliation(s)
- Manuel Morante
- Dept. of Electronic Systems, Aalborg University, Denmark; Computer Technology Institutes & Press "Diophantus" (CTI), Patras, Greece.
| | | | - Christos Chatzichristos
- Dept. Electrical Engineering (ESAT), Dynamical Systems, Signal Processing and Data Analytics (STADIUS), KU Leuven, Belgium
| | | | - Sergios Theodoridis
- Dept. of Electronic Systems, Aalborg University, Denmark; Dept. of Informatics and Telecommunications of the National and Kapodistrian University of Athens, Greece
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Taylor JA, Larsen KM, Dzafic I, Garrido MI. Predicting subclinical psychotic-like experiences on a continuum using machine learning. Neuroimage 2021; 241:118329. [PMID: 34302968 DOI: 10.1016/j.neuroimage.2021.118329] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/01/2021] [Indexed: 11/18/2022] Open
Abstract
Previous studies applying machine learning methods to psychosis have primarily been concerned with the binary classification of chronic schizophrenia patients and healthy controls. The aim of this study was to use electroencephalographic (EEG) data and pattern recognition to predict subclinical psychotic-like experiences on a continuum between these two extremes in otherwise healthy people. We applied two different approaches to an auditory oddball regularity learning task obtained from N = 73 participants: A feature extraction and selection routine incorporating behavioural measures, event-related potential components and effective connectivity parameters; Regularisation of spatiotemporal maps of event-related potentials. Using the latter approach, optimal performance was achieved using the response to frequent, predictable sounds. Features within the P50 and P200 time windows had the greatest contribution toward lower Prodromal Questionnaire (PQ) scores and the N100 time window contributed most to higher PQ scores. As a proof-of-concept, these findings demonstrate that EEG data alone are predictive of individual psychotic-like experiences in healthy people. Our findings are in keeping with the mounting evidence for altered sensory responses in schizophrenia, as well as the notion that psychosis may exist on a continuum expanding into the non-clinical population.
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Affiliation(s)
- Jeremy A Taylor
- Melbourne School of Psychological Sciences, University of Melbourne, Australia; Queensland Brain Institute, University of Queensland, Australia.
| | - Kit Melissa Larsen
- Queensland Brain Institute, University of Queensland, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark; Child and Adolescent Mental Health Care, Mental Health Services Capital Region Copenhagen, University of Copenhagen, Denmark
| | - Ilvana Dzafic
- Melbourne School of Psychological Sciences, University of Melbourne, Australia; Queensland Brain Institute, University of Queensland, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function; Centre for Advanced Imaging, University of Queensland, Australia
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, University of Melbourne, Australia; Queensland Brain Institute, University of Queensland, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function; Centre for Advanced Imaging, University of Queensland, Australia
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Wu Y, Wu X, Wei Q, Wang K, Tian Y. Differences in Cerebral Structure Associated With Depressive Symptoms in the Elderly With Alzheimer's Disease. Front Aging Neurosci 2020; 12:107. [PMID: 32477094 PMCID: PMC7236549 DOI: 10.3389/fnagi.2020.00107] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 03/30/2020] [Indexed: 11/13/2022] Open
Abstract
Background: Alzheimer's disease (AD) is characterized by global deterioration in multiple cognitive domains. In addition to cognitive impairment, depressive symptoms are common issues that trouble AD patients. The neuroanatomical basis of depressive symptoms in AD patients has yet to be elucidated. Method: Twenty AD patients and 22 healthy controls (HCs) were recruited for the present study. Depressive symptoms in AD patients and HCs were assessed according to the Hamilton Depression Rating Scale (HDRS). Anatomical structural differences were assessed between AD patients and HCs using voxel-based morphometry (VBM) and surface-based morphometry (SBM). Correlation analyses were conducted to investigate relationships between depressive symptoms and structural altered regions. Multiple pattern analysis using linear support vector machine (SVM) was performed in another independent cohort, which was collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) data and contained 20 AD patients and 20 HCs, to distinguish AD patients from HCs. Results: Compared with HCs, AD patients exhibited global cerebral atrophy in gray matter volume (GMV) and cortical thickness, including frontal, parietal, temporal, occipital, and insular lobes. In addition, insular GMV was negatively correlated with depressive symptoms. Moreover, SVM-based classification achieved an accuracy of 77.5%, a sensitivity of 70%, and a specificity of 85% by leave-one-out cross-validation. Conclusion: GMV of the insula displayed atrophy among AD patients, which is associated with depressive symptoms. Our observations provide a potential neural substrate for analysis to examine the co-occurrence of AD with depressive symptoms.
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Affiliation(s)
- Yue Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Xingqi Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Qiang Wei
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
- Department of Medical Psychology, Chaohu Clinical Medical College, Anhui Medical University, Hefei, China
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
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Sanchez-Lopez R, Fereczkowski M, Neher T, Santurette S, Dau T. Robust Data-Driven Auditory Profiling Towards Precision Audiology. Trends Hear 2020; 24:2331216520973539. [PMID: 33272110 PMCID: PMC7720332 DOI: 10.1177/2331216520973539] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 10/12/2020] [Accepted: 10/15/2020] [Indexed: 12/13/2022] Open
Abstract
The sources and consequences of a sensorineural hearing loss are diverse. While several approaches have aimed at disentangling the physiological and perceptual consequences of different etiologies, hearing deficit characterization and rehabilitation have been dominated by the results from pure-tone audiometry. Here, we present a novel approach based on data-driven profiling of perceptual auditory deficits that attempts to represent auditory phenomena that are usually hidden by, or entangled with, audibility loss. We hypothesize that the hearing deficits of a given listener, both at hearing threshold and at suprathreshold sound levels, result from two independent types of "auditory distortions." In this two-dimensional space, four distinct "auditory profiles" can be identified. To test this hypothesis, we gathered a data set consisting of a heterogeneous group of listeners that were evaluated using measures of speech intelligibility, loudness perception, binaural processing abilities, and spectrotemporal resolution. The subsequent analysis revealed that distortion type-I was associated with elevated hearing thresholds at high frequencies and reduced temporal masking release and was significantly correlated with elevated speech reception thresholds in noise. Distortion type-II was associated with low-frequency hearing loss and abnormally steep loudness functions. The auditory profiles represent four robust subpopulations of hearing-impaired listeners that exhibit different degrees of perceptual distortions. The four auditory profiles may provide a valuable basis for improved hearing rehabilitation, for example, through profile-based hearing-aid fitting.
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Affiliation(s)
- Raul Sanchez-Lopez
- Hearing Systems Section, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Michal Fereczkowski
- Hearing Systems Section, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
- Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
- Research Unit for Oto-Rhino-Laryngology, Odense University Hospital, Odense, Denmark
| | - Tobias Neher
- Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
- Research Unit for Oto-Rhino-Laryngology, Odense University Hospital, Odense, Denmark
| | - Sébastien Santurette
- Hearing Systems Section, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
- Centre for Applied Audiology Research, Oticon A/S, Smørum, Denmark
| | - Torsten Dau
- Hearing Systems Section, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
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Krohne LG, Wang Y, Hinrich JL, Moerup M, Chan RCK, Madsen KH. Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task. Hum Brain Mapp 2019; 40:4965-4981. [PMID: 31403748 PMCID: PMC6865405 DOI: 10.1002/hbm.24751] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 07/22/2019] [Accepted: 07/26/2019] [Indexed: 02/01/2023] Open
Abstract
Previous studies have suggested that the degree of social anhedonia reflects the vulnerability for developing schizophrenia. However, only few studies have investigated how functional network changes are related to social anhedonia. The aim of this fMRI study was to classify subjects according to their degree of social anhedonia using supervised machine learning. More specifically, we extracted both spatial and temporal network features during a social cognition task from 70 subjects, and used support vector machines for classification. Since impairment in social cognition is well established in schizophrenia-spectrum disorders, the subjects performed a comic strip task designed to specifically probe theory of mind (ToM) and empathy processing. Features representing both temporal (time series) and network dynamics were extracted using task activation maps, seed region analysis, independent component analysis (ICA), and a newly developed multi-subject archetypal analysis (MSAA), which here aimed to further bridge aspects of both seed region analysis and decomposition by incorporating a spotlight approach.We found significant classification of subjects with elevated levels of social anhedonia when using the times series extracted using MSAA, indicating that temporal dynamics carry important information for classification of social anhedonia. Interestingly, we found that the same time series yielded the highest classification performance in a task classification of the ToM condition. Finally, the spatial network corresponding to that time series included both prefrontal and temporal-parietal regions as well as insula activity, which previously have been related schizotypy and the development of schizophrenia.
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Affiliation(s)
- Laerke Gebser Krohne
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKgs. LyngbyDenmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University HospitalHvidovreDenmark
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
| | - Jesper L. Hinrich
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKgs. LyngbyDenmark
| | - Morten Moerup
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKgs. LyngbyDenmark
| | - Raymond C. K. Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
| | - Kristoffer H. Madsen
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKgs. LyngbyDenmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University HospitalHvidovreDenmark
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
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