Multivariate Pattern Analysis Research Articles

The Impact of Ageing on Episodic Memory Retrieval: How Valence Influences Neural Functional Connectivity

Age-related decline in episodic memory is often linked to structural and functional changes in the brain. Here, we investigated how these alterations might affect functional connectivity during memory retrieval following exposure to emotional stimuli. Using functional magnetic resonance imaging (fMRI), participants viewed images with varying emotional valences (positive, negative, and neutral) followed by unrelated non-arousing videos and were then asked to retrieve an episodic detail from the previously shown video. We conducted Multivariate Pattern Analysis (MVPA) to identify regions with divergent responses between age groups, which then served as seeds in Seed-Based Connectivity (SBC) analyses. The results revealed an age-related decline in behavioural performance following exposure to negative stimuli but preserved performance following positive stimuli. Young adults exhibited increased functional connectivity following negative valence. Conversely, old adults displayed increased connectivity more scarcely, and only following positive valence. These findings point to an adaptive response of the impact of emotions on task performance that depends on neural adaptations related to ageing. This suggests that age-related changes in functional connectivity might underlie how emotions influence memory, highlighting the need to tailor memory support strategies in older adulthood.

A distributed brain response predicting the facial expression of acute nociceptive pain.

Pain is a private experience observable through various verbal and non-verbal behavioural manifestations, each of which may relate to different pain-related functions. Despite the importance of understanding the cerebral mechanisms underlying those manifestations, there is currently limited knowledge of the neural correlates of the facial expression of pain. In this functional magnetic resonance imaging (fMRI) study, noxious heat stimulation was applied in healthy volunteers and we tested if previously published brain signatures of pain were sensitive to pain expression. We then applied a multivariate pattern analysis to the fMRI data to predict the facial expression of pain. Results revealed the inability of previously developed pain neurosignatures to predict the facial expression of pain. We thus propose a facial expression of pain signature (FEPS) conveying distinctive information about the brain response to nociceptive stimulations with minimal or no overlap with other pain-relevant brain signatures associated with nociception, pain ratings, thermal pain aversiveness, or pain valuation. The FEPS may provide a distinctive functional characterization of the distributed cerebral response to nociceptive pain associated with the socio-communicative role of non-verbal pain expression. This underscores the complexity of pain phenomenology by reinforcing the view that neurosignatures conceived as biomarkers must be interpreted in relation to the specific pain manifestation(s) predicted and their underlying function(s). Future studies should explore other pain-relevant manifestations and assess the specificity of the FEPS against simulated pain expressions and other types of aversive or emotional states.

A distributed brain response predicting the facial expression of acute nociceptive pain

Pain is a private experience observable through various verbal and non-verbal behavioural manifestations, each of which may relate to different pain-related functions. Despite the importance of understanding the cerebral mechanisms underlying those manifestations, there is currently limited knowledge of the neural correlates of the facial expression of pain. In this functional magnetic resonance imaging (fMRI) study, noxious heat stimulation was applied in healthy volunteers and we tested if previously published brain signatures of pain were sensitive to pain expression. We then applied a multivariate pattern analysis to the fMRI data to predict the facial expression of pain. Results revealed the inability of previously developed pain neurosignatures to predict the facial expression of pain. We thus propose a facial expression of pain signature (FEPS) conveying distinctive information about the brain response to nociceptive stimulations with minimal or no overlap with other pain-relevant brain signatures associated with nociception, pain ratings, thermal pain aversiveness, or pain valuation. The FEPS may provide a distinctive functional characterization of the distributed cerebral response to nociceptive pain associated with the socio-communicative role of non-verbal pain expression. This underscores the complexity of pain phenomenology by reinforcing the view that neurosignatures conceived as biomarkers must be interpreted in relation to the specific pain manifestation(s) predicted and their underlying function(s). Future studies should explore other pain-relevant manifestations and assess the specificity of the FEPS against simulated pain expressions and other types of aversive or emotional states.

Multitasking practice eliminates modality-based interference by separating task representations in sensory brain regions.

The debate on the neural basis of multitasking costs evolves around neural overlap between concurrently performed tasks. Recent evidence suggests that training-related reductions in representational overlap in fronto-parietal brain regions predict multitasking improvements. Cognitive theories assume that overlap of task representations may lead to unintended information exchange between tasks (i.e., crosstalk). Modality-based crosstalk was suggested as a source for multitasking costs in multisensory settings. Robust findings of increased costs for certain modality mappings may be explained by crosstalk between the stimulus modality in one task and sensory action consequences in the concurrently performed task. Whether modality-based crosstalk emerges from representational overlap in general fronto-parietal multitasking regions or modality-specific regions is not known yet. In this functional neuroimaging study, we investigate neural overlap during multitasking performance in humans, focusing on modality compatibility by employing multivariate pattern analysis and modality-specific practice interventions in three groups (total N = 54, 24 females). We observed significant differences between modality compatible and modality incompatible single-task representations, specifically in the auditory cortex but not in fronto-parietal regions. Notably, improved auditory decoding accuracy related to modality incompatible tasks was predictive of performance gains in the corresponding dual task along with complete elimination of modality-specific dual-task costs. This predictive relationship was evident only in the group practicing modality incompatible mappings, suggesting that specific practice on task sets with modality overlap influenced both neural representations and subsequent multitasking performance. This study contributes to the integration of cognitive theory and neuroscience and the role of task representations in dual-task interference.Significance Statement In a society dominated by multitasking, understanding its neurocognitive basis and plasticity is crucial for key aspects of everyday tasks. We investigate the neural mechanisms behind multitasking limitations, offering insights for targeted cognitive interventions. The study builds upon established theories of cognitive multitasking and imaging research, addressing the concept of modality-based crosstalk - the unintended exchange of modality-based information between tasks. Through functional brain imaging and pattern analysis, we examined how neural task representations contribute to performance costs in dual tasks with varying degrees of modality overlap. Notably, our findings demonstrate a practice-related decrease in neural overlap which is associated with substantial multitasking improvements, specifically in the auditory cortex, emphasizing the contribution of sensory regions to flexible multidimensional task representations.

Seed dispersal mechanisms modulate Janzen‐Connell effects in Mediterranean forests through antagonists and mutualists

Abstract Antagonist and mutualist organisms mediate the long‐standing, classical Janzen‐Connell effects and yet they are rarely investigated, especially when they share plant species hosts. Here, we propose that plant species create an “antagonists‐ and mutualist‐scape” which, coupled with seed dispersal mechanisms, will govern the relative survival rates of juveniles within the landscape and influence their later life stages. We combined data on fully stem mapped plots of woody plant species in two Mediterranean forests (Mixed Forest of Segura [MFS] and Mixed Forest of Jaen [MFJ]) with data on the associated communities of leaf epiphytic fungi, leaf pathogens, sap‐sucking insects and chewer insects hosted by each woody plant species. This allowed us to indirectly test the Janzen‐Connell hypothesis by investigating the placement of plant species with contrasting seed dispersal mechanisms (fleshy fruited vs. dry‐fruited plant) within the antagonists‐ and mutualist‐scapes. More specifically, we determined the degree of host‐specificity of plant‐associated organisms and defined the antagonist‐ or mutualist‐scapes for focal plants as the mean number of antagonist‐ or mutualist species the focal plant shares with its (sapling or adult) plant neighbours, respectively. We then applied multivariate spatial point pattern analysis to test whether saplings and adults of the focal species tended to be located in favourable sites of the antagonist‐scapes (i.e. were neighboured by fewer antagonist species than expected) or were distributed independently of the antagonist‐scapes. A substantial proportion of organisms was associated with more than one plant species, with organisms at MFJ being more “host‐specific” than organisms at MFS. Our spatial analyses revealed spatial patterns that differed strongly between fleshy‐fruited and dry‐fruited species and were, in most cases, consistent with our expectations. For example, saplings of dry‐fruited species were placed in favourable sites in the saplings antagonist‐scapes, whereas saplings of fleshy‐fruited species were independently placed with respect to the saplings antagonist‐scapes. Our results show how the placement of plant species within the antagonist‐scapes changes during the ontogeny, and how this is influenced by Janzen‐Connell effects and the seed dispersal mechanisms. We provide a new approach and indications about the importance of generalist antagonists and mutualists as drivers of the spatial assembly of plant communities. Read the free Plain Language Summary for this article on the Journal blog.

Pain-Discriminating Information Decoded From Spatiotemporal Patterns of Hemodynamic Responses Measured by fMRI in the Human Brain.

Functional magnetic resonance imaging (fMRI) has been widely used in studying the neural mechanisms of pain in the human brain, primarily focusing on where in the brain pain-elicited neural activities occur (i.e., the spatial distribution of pain-related brain activities). However, the temporal dynamics of pain-elicited hemodynamic responses (HDRs) measured by fMRI may also contain information specific to pain processing but have been largely neglected. Using high temporal resolution fMRI (TR = 0.8 s) data acquired from 62 healthy participants, in the present study we aimed to test whether pain-distinguishing information could be decoded from the spatial pattern of the temporal dynamics (i.e., the spatiotemporal pattern) of HDRs elicited by painful stimuli. Specifically, the peak latency and the response duration were used to characterize the temporal dynamics of HDRs to painful laser stimuli and non-painful electric stimuli, and then were compared between the two conditions (i.e., pain and no-pain) using a voxel-wise univariate analysis and a multivariate pattern analysis. Furthermore, we also tested whether the two temporal characteristics of pain-elicited HDRs and their spatial patterns were associated with pain-related behaviors. We found that the spatial patterns of HDR peak latency and response duration could successfully discriminate pain from no-pain. Interestingly, we also observed that the Pain Vigilance and Awareness Questionnaire (PVAQ) scores were correlated with the average response duration in bilateral insula and secondary somatosensory cortex (S2) and could also be predicted from the across-voxel spatial patterns of response durations in the middle cingulate cortex and middle frontal gyrus only during painful condition but not during non-painful condition. These findings indicate that the spatiotemporal pattern of pain-elicited HDRs may contain pain-specific information and highlight the importance of studying the neural mechanisms of pain by taking advantage of the high sensitivity of fMRI to both spatial and temporal information of brain responses.

Fast feature- and category-related parafoveal previewing support free visual exploration.

While humans typically saccade every ∼250 ms in natural settings, studies on vision tend to prevent or restrict eye movements. As it takes ∼50 ms to initiate and execute a saccade, this leaves only ∼200 ms to identify the fixated object and select the next saccade goal. How much detail can be derived about parafoveal objects in this short time interval, during which foveal processing and saccade planning both occur? Here, we had male and female human participants freely explore a set of natural images while we recorded magnetoencephalography and eye movements. Using multivariate pattern analysis, we demonstrate that future parafoveal images could be decoded at the feature and category level with peak decoding at ∼110 ms and ∼165 ms respectively, while the decoding of fixated objects at the feature and category level peaked at ∼100 ms and ∼145 ms. The decoding of features and categories was contingent on the objects being saccade goals. In sum, we provide insight on the neuronal mechanism of pre-saccadic attention by demonstrating that feature and category specific information of foveal and parafoveal objects can be extracted in succession within a ∼200 ms intersaccadic interval. These findings rule out strict serial or parallel processing accounts but are consistent with a pipeline mechanism in which foveal and parafoveal objects are processed in parallel but at different levels in the visual hierarchy.Significance Statement We provide neural evidence that future parafoveal saccade goals are processed surprisingly quickly at the feature and the category level before we saccade to them. Specifically, using multivariate pattern analysis applied to magnetoencephalography and eye-tracking data, we found that information about the colour and the category of parafoveal objects emerged at ∼110 ms and ∼165 ms respectively, with the same information about foveal objects emerging ∼100 ms and ∼145 ms. Our findings provide novel insight into the neuronal dynamics of parafoveal previewing during free visual exploration. The dynamics rule out strict serial or parallel processing, but are consistent with a pipelining mechanism in which foveal and parafoveal objects are processed in parallel but at different levels in the visual hierarchy.

Neurocognitive dynamics of preparatory and adaptive cognitive control: Insights from mass-univariate and multivariate pattern analysis of EEG data.

Cognitive control refers to humans' ability to willingly align thoughts and actions with internally represented goals. Research indicates that cognitive control is not one-dimensional but rather integrates multiple sub-processes to cope with task demands successfully. In particular, the dynamic interplay between preparatory (i.e., prior to goal-relevant events) and adaptive (i.e., in response to unexpected demands) recruitment of neural resources is believed to facilitate successful behavioural performance. However, whether preparatory and adaptive processes draw from independent or shared neural resources, and how these align in the information processing stream, remains unclear. To address these issues, we recorded electroencephalographic data from 52 subjects while they performed a computerised task. Using a combination of mass-univariate and multivariate pattern analysis procedures, we found that different types of control triggered distinct sequences of brain activation patterns, and that the order and temporal extent of these patterns were dictated by the type of control used by the participants. Stimuli that fostered preparatory recruitment of control evoked a sequence of transient occipital-parietal, sustained central-parietal, and sustained fronto-central responses. In contrast, stimuli that indicated the need for quick behavioural adjustments triggered a sequence of transient occipital-parietal, fronto-central, and central parietal responses. There was also a considerable degree of overlap in the temporal evolution of these brain activation patterns, with behavioural performance being mainly related to the magnitude of the central-parietal and fronto-central responses. Our results demonstrate how different neurocognitive mechanisms, such as early attentional allocation and subsequent behavioural selection processes, are likely to contribute to cognitive control. Moreover, our findings extend prior work by showing that these mechanisms are engaged (at least partly) in parallel, rather than independently of each other.

Decoding and modifying dynamic attentional bias in gaming disorder.

With the spread of smartphones and computer games, concerns have escalated regarding the rising prevalence of gaming disorder. Patients often display attentional biases, unconsciously turning their attention towards gaming-related stimuli. However, attempts to discover and ameliorate these attentional deficits have yielded inconsistent outcomes, potentially due to the dynamic nature of attentional bias. This study investigated neural mechanisms underlying attentional bias state by combining neuroimaging (functional magnetic resonance imaging -fMRI) with an approach-avoidance task tailored to an individual's gaming preference. We conducted a multivariate pattern analysis of endogenous brain activity in 21 participants with probable gaming disorder. Our analyses revealed that activity patterns in the insula tracked temporal attentional bias states specific to gaming stimuli. A broad network of frontal and parietal regions instead appeared to predict a general temporal attentional bias state. Finally, we conducted a proof-of-concept study for 'just-in-time' attentional bias training through fMRI-decoded neurofeedback of insula activity patterns, named decoded attentional bias training (DecABT). Our preliminary results suggest that DecABT may help to decrease the attractiveness of gaming stimuli via a insula- and precuneus-based neural mechanism. This work provides new evidence for the insula as an endogenous regulator of attentional bias states in gaming disorder and a starting point to develop novel, individualized therapeutic approaches to treat addiction.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Revealing the different levels of action monitoring in visuomotor transformation task: Evidence from decomposition of cortical potentials.

This study investigates the cortical correlates of motor response control and monitoring, using the Theory of Event Coding (TEC)as a framework to investigate signals related to low-level sensory processing of motor reafference and high-level response monitoring, including verification of response outcomes with the internal model. We used a visuomotor paradigm with two targets at different distances from the participant. For the recorded movement-related cortical potentials (MRCPs), we analyzed their different components and assessed the movement phases during which they are active. Residual iteration decomposition (RIDE) and multivariate pattern analysis (MVPA) were used for this analysis. Using RIDE, we separated MRCPs into signals related to different parallel processes of visuomotor transformation: stimulus processing (S-cluster), motor response preparation and execution (R-cluster), and intermediate processes (C-cluster). We revealed sequential activation in the R-cluster, with execution-related negative components and positive contralateral peaks reflecting reafference processing. We also identified the motor post-imperative negative variation within the R-cluster, highlighting the response outcome evaluation process included in the action file. Our findings extend the understanding of C-cluster signals, typically associated with stimulus-response mapping, by demonstrating C-activation from the preparatory stages through to response termination, highlighting its participation in action monitoring. In addition, we highlighted the ability of MVPA to identify movement-related attribute encoding: where statistical analysis showed independence of stimulus processing activity from movement distance, MVPA revealed distance-related differences in the S-cluster within a time window aligned with the lateralized readiness potential (LRP). This highlights the importance of integrating RIDE and MVPA to uncover the intricate neural dynamics of motor control, sensory integration, and response monitoring.

No evidence that visual impulses enhance the readout of retrieved long-term memory contents from EEG activity

Abstract The application of multivariate pattern analysis (MVPA) to electroencephalography (EEG) data allows neuroscientists to track neural representations at temporally fine-grained scales. This approach has been leveraged to study the locus and evolution of long-term memory contents in the brain, but a limiting factor is that decoding performance remains low. A key reason for this is that processes like encoding and retrieval are intrinsically dynamic across trials and participants, and this runs in tension with MVPA and other techniques that rely on consistently unfolding neural codes to generate predictions about memory contents. The presentation of visually perturbing stimuli may experimentally regularize brain dynamics, making neural codes more stable across measurements to enhance representational readouts. Such enhancements, which have repeatedly been demonstrated in working memory contexts, could offer a tool to improve decoding in long-term memory tasks. In this study, we evaluated whether visual perturbations—or pings—improve our ability to predict the category of retrieved images from EEG activity during cued recall. Overall, our findings suggest that while pings evoked a prominent neural response, they did not reliably produce improvements in MVPA-based classification across several analyses. We discuss possibilities that could explain these results, including the role of experimental and analysis parameter choices and mechanistic differences between working and long-term memory.

Suppressing the Maintenance of Information in Working Memory Alters Long-term Memory Traces.

The sensory recruitment hypothesis conceptualizes information in working memory as being activated representations of information in long-term memory. Accordingly, changes made to an item in working memory would be expected to influence its subsequent retention. Here, we tested the hypothesis that suppressing information from working memory, which can reduce short-term access to that information, may also alter its long-term neural representation. We obtained fMRI data (n = 25; 13 female / 12 male participants) while participants completed a working memory removal task with scene images as stimuli, followed by a final surprise recognition test of the examined items. We applied a multivariate pattern analysis to the data to quantify the engagement of suppression on each trial, to track the contents of working memory during suppression, and to assess representational changes afterward. Our analysis confirms previous reports that suppression of information in working memory involves focused attention to target and remove unwanted information. Furthermore, our findings provide new evidence that even a single dose of suppression of an item in working memory can (if engaged with sufficient strength) produce lasting changes in its neural representation, particularly weakening the unique, item-specific features, which leads to forgetting. Our study sheds light on the underlying mechanisms that contribute to the suppression of unwanted thoughts and highlights the dynamic interplay between working memory and long-term memory.

Word Type and Frequency Effects on Lexical Decisions Are Process-dependent and Start Early.

When encountering letter strings, we rapidly determine whether they are words. The speed of such lexical decisions (LDs) is affected by word frequency. Apart from influencing late, decision-related, processing stages, frequency has also been shown to affect very early stages, and even the processing of nonwords. We developed a detailed account of the different frequency effects involved in LDs by (1) dividing LDs into processing stages using a combination of hidden semi-Markov models and multivariate pattern analysis applied to EEG data and (2) using generalized additive mixed models to investigate how the effect of continuous word and nonword frequency differs between these stages. We discovered six stages shared between word types, with the fifth stage consisting of two substages for pseudowords only. In the earliest stages, visual processing was completed faster for frequent words, but took longer for word-like nonwords. Later stages involved an orthographic familiarity assessment followed by an elaborate decision process, both affected differently by frequency. We therefore conclude that frequency indeed affects all processes involved in LDs and that the magnitude and direction of these effects differ both by process and word type.

Distinct connectivity patterns in bipolar and unipolar depression: a functional connectivity multivariate pattern analysis study

BackgroundPatients with bipolar disorder (BD) and major depressive disorder (MDD) exhibit depressive episodes with similar symptoms despite having different and poorly understood underlying neurobiology, often leading to misdiagnosis and improper treatment. This exploratory study examined whole-brain functional connectivity (FC) using FC multivariate pattern analysis (fc-MVPA) to identify the FC patterns with the greatest ability to distinguish between currently depressed patients with BD type I (BD I) and those with MDD.MethodologyIn a cross-sectional design, 41 BD I, 40 MDD patients and 63 control participants completed resting state functional magnetic resonance imaging scans. Data-driven fc-MVPA, as implemented in the CONN toolbox, was used to identify clusters with differential FC patterns between BD patients and MDD patients. The identified cluster was used as a seed in a post hoc seed-based analysis (SBA) to reveal associated connectivity patterns, followed by a secondary ROI-to-ROI analysis to characterize differences in connectivity between these patterns among BD I patients, MDD patients and controls.ResultsFC-MVPA identified one cluster located in the right frontal pole (RFP). The subsequent SBA revealed greater FC between the RFP and posterior cingulate cortex (PCC) and between the RFP and the left inferior/middle temporal gyrus (LI/MTG) and lower FC between the RFP and the left precentral gyrus (LPCG), left lingual gyrus/occipital cortex (LLG/OCC) and right occipital cortex (ROCC) in MDD patients than in BD patients. Compared with the controls, ROI-to-ROI analysis revealed lower FC between the RFP and the PCC and greater FC between the RFP and the LPCG, LLG/OCC and ROCC in BD patients; in MDD patients, the analysis revealed lower FC between the RFP and the LLG/OCC and ROCC and greater FC between the RFP and the LI/MTG.ConclusionsDifferences in the RFP FC patterns between currently depressed patients with BD and those with MDD suggest potential neuroimaging markers that should be further examined. Specifically, BD patients exhibit increased FC between the RFP and the motor and visual networks, which is associated with psychomotor symptoms and heightened compensatory frontoparietal FC to counter distractibility. In contrast, MDD patients exhibit increased FC between the RFP and the default mode network, corresponding to sustained self-focus and rumination.

Single-dose psilocybin alters resting state functional networks in patients with body dysmorphic disorder

Body dysmorphic disorder (BDD) is a severe psychiatric condition characterized by preoccupation with perceived flaws in one's appearance, which the individual views as defective or ugly. Psilocybin, a serotonin 2A receptor agonist with psychedelic properties, has emerged as a potential therapeutic agent for depression and other psychiatric disorders. This study aimed to identify subacute neural changes predicting symptomatic response to psilocybin treatment in adults with BDD. Eight adults with moderate-to-severe nondelusional BDD were administered a single oral 25 mg dose of psilocybin, accompanied by psychological support, and underwent resting state functional magnetic resonance imaging assessments 1 day before and 1 day after the dosing. Both a region of interest (ROI)-to-ROI analysis and multivariate pattern analysis (MVPA) were used to identify changes in resting state functional connectivity (rsFC) at day 1 after dosing that predicted treatment response at week 1, measured by change in Yale-Brown Obsessive Compulsive Disorder Scale Modified for BDD (BDD-YBOCS) score. All participants completed the dosing and follow-up assessments over 12 weeks. BDD-YBOCS scores decreased at week 1 and week 12 after dosing (p<0.001 for both). MVPA revealed a significant increase in rsFC within the Executive Control Network (ECN) at day 1. Increased rsFC within the ECN (dlPFC – Superior Parietal Lobule [FPL]), between the ECN and Default Mode Network (dlPFC – Precuneus), and between the ECN and the Salience Network (dlPFC – insula) were predictive of improvement in BDD symptoms at week 1. These findings are the first report of subacute brain effects of psilocybin in patients with BDD. Given the small sample size and uncontrolled design of the study, larger controlled studies are necessary to validate these observations. Clinical Trials Registration: Clinicaltrials.gov ID: NCT04656301

Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.

Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in brain function and structure, overcoming the limitations of traditional univariate methods. To assess the reliability of neuroimaging-based biomarkers and the contribution of study characteristics in distinguishing individuals with schizophrenia spectrum disorder (SSD) from healthy controls (HCs), we conducted a systematic review of the studies that used multivariate pattern recognition for this objective. We systematically searched PubMed, Scopus, and Web of Science for studies on SSD classification using multivariate pattern analysis on magnetic resonance imaging data. We employed a bivariate random-effects meta-analytic model to explore the classification of sensitivity (SE) and specificity (SP) across studies while also evaluating the moderator effects of clinical and non-clinical variables. A total of 119 studies (with 12,723 patients with SSD and 13,196 HCs) were identified. The meta-analysis estimated a SE of 79.1% (95% confidence interval [CI], 77.1%-81.0%) and a SP of 80.0% (95% CI, 77.8%-82.0%). In particular, the Positive and Negative Syndrome Scale and the Global Assessment of Functioning scores, age, age of onset, duration of untreated psychosis, deep learning, algorithm type, features selection, and validation methods had significant effects on classification performance. Multivariate pattern analysis reliably identifies neuroimaging-based biomarkers of SSD, achieving ∼80% SE and SP. Despite clinical heterogeneity, discernible brain modifications effectively differentiate SSD from HCs. Classification performance depends on patient-related and methodological factors crucial for the development, validation, and application of prospective models in clinical settings.

Optimizing magnetometers arrays and analysis pipelines for multivariate pattern analysis

BackgroundMultivariate pattern analysis (MVPA) has proven an excellent tool in cognitive neuroscience. It also holds a strong promise when applied to optically-pumped magnetometer-based magnetoencephalography. New methodTo optimize OPM-MEG systems for MVPA experiments this study examines data from a conventional MEG magnetometer array, focusing on appropriate noise reduction techniques for magnetometers. We determined the least required number of sensors needed for robust MVPA for image categorization experiments. ResultsWe found that the use of signal space separation (SSS) without a proper regularization significantly lowered the classification accuracy considering a sub-array of 102 magnetometers or a sub-array of 204 gradiometers. We also found that classification accuracy did not improve when going beyond 30 sensors irrespective of whether SSS has been applied. Comparison with existing methodsThe power spectra of data filtered with SSS has a substantially higher noise floor that data cleaned with SSP or HFC. Consequently, MVPA decoding results obtained from the SSS-filtered data are significantly lower compared to all other methods employed. ConclusionsWhen designing MEG system based on SQUID magnetometers optimized for multivariate analysis for image categorization experiments, about 30 magnetometers are sufficient. We advise against applying SSS filters without a proper regularization to data from MEG and OPM systems prior to performing MVPA as this method, albeit reducing low-frequency external noise contributions, also introduces an increase in broadband noise. We recommend employing noise reduction techniques that either decrease or maintain the noise floor of the data like signal-space projection, homogeneous field correction and gradient noise reduction.

The impact of predicted and unpredicted events on the spatial priority map: evidence from multivariate pattern analyses in EEG

The impact of predicted and unpredicted events on the spatial priority map: evidence from multivariate pattern analyses in EEG

Attention Modulates Human Visual Responses to Objects by Tuning Sharpening.

Visual stimuli compete with each other for cortical processing and attention biases this competition in favor of the attended stimulus. How does the relationship between the stimuli affect the strength of this attentional bias? Here, we used functional MRI to explore the effect of target-distractor similarity in neural representation on attentional modulation in the human visual cortex using univariate and multivariate pattern analyses. Using stimuli from four object categories (human bodies, cats, cars and houses), we investigated attentional effects in the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA. We demonstrated that the strength of the attentional bias towards the target is not fixed but decreases with increasing target-distractor similarity. Simulations provided evidence that this result pattern is explained by tuning sharpening rather than an increase in gain. Our findings provide a mechanistic explanation for behavioral effects of target-distractor similarity on attentional biases and suggest tuning sharpening as the underlying mechanism in object-based attention.

Disturbed Dynamic Brain Activity and Neurovascular Coupling in End-Stage Renal Disease Assessed With MRI.

Pathophysiological mechanisms underlying cognitive impairment in end-stage renal disease (ESRD) remain unclear, with limited studies on the temporal variability of neural activity and its coupling with regional perfusion. To assess neural activity and neurovascular coupling (NVC) in ESRD patients, evaluate the classification performance of these abnormalities, and explore their relationships with cognitive function. Prospective. Exactly 33 ESRD patients and 35 age, sex, and education matched healthy controls (HCs). The 3.0T/3D pseudo-continuous arterial spin labeling, resting-state functional MRI, and 3D-T1 weighted structural imaging. Dynamic (dfALFF) and static (sfALFF) fractional amplitude of low-frequency fluctuations and cerebral blood flow (CBF) were assessed. CBF-fALFF correlation coefficients and CBF/fALFF ratio were determined for ESRD patients and HCs. Their ability to distinguish ESRD patients from HCs was evaluated, alongside assessment of cerebral small vessel disease (CSVD) MRI features. All participants underwent blood biochemical and neuropsychological tests to evaluate cognitive decline. Chi-squared test, two-sample t-test, Mann-Whitney U tests, covariance analysis, partial correlation analysis, family-wise error, false discovery rate, Bonferroni correction, area under the receiver operating characteristic curve (AUC) and multivariate pattern analysis. P < 0.05 denoted statistical significance. ESRD patients exhibited higher dfALFF in triangular part of left inferior frontal gyrus (IFGtriang) and left middle temporal gyrus, lower CBF/dfALFF ratio in multiple brain regions, and decreased CBF/sfALFF ratio in bilateral superior temporal gyrus (STG). Compared with CBF/sfALFF ratio, dfALFF, and sfALFF, CBF/dfALFF ratio (AUC = 0.916) achieved the most powerful classification performance in distinguishing ESRD patients from HCs. In ESRD patients, decreased CBF/fALFF ratio correlated with more severe renal impairment, increased CSVD burden, and cognitive decline (0.4 < |r| < 0.6). ESRD patients exhibited abnormal dynamic brain activity and impaired NVC, with dynamic features demonstrating superior discriminative capacity and CBF/dfALFF ratio showing powerful classification performance. 1 TECHNICAL EFFICACY: Stage 1.