Wiley

Human Brain Mapping

Published by Wiley

Online ISSN: 1097-0193

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Print ISSN: 1065-9471

Disciplines: Neuroscience

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Task design and stimulation setup. (A) In the confidence accuracy task, participants chose between an immediately available smaller‐sooner reward (e.g., 5 CHF today) and a larger‐later reward (e.g., 10 CHF in 20 days). After each decision, they were asked to indicate their confidence in having made the best choice on a scale from 0 to 7. (B) In the mentalizing (false‐belief) task, participants first viewed two consecutive images: in the first image, a person named Jack was holding a ball above one of two hats, while another person named Jill was watching. In the second image, Jack placed the ball in one of the two hats while Jill was absent. Participants had to indicate the position of the ball from either their own or Jill's perspective. Jill held either a true belief (position did not switch) or a false belief (position switched). Participants performed the confidence accuracy and mentalizing tasks in the MRI scanner in an interleaved way. (C) During task performance in the scanner, participants received theta (5 Hz), alpha (10 Hz), or sham tACS using a 3 × 1 electrode setup over the FPC. We estimated the electric field density (normE = volts per meter, V/m) with Simnibs 2.1 (current strength of 4 mA at central electrode). Warmer colors indicate higher electric field density. (D) According to two‐layer accounts of metacognition, the FPC enables accurate metacognitive judgments by reading‐out decision‐related information from other brain regions involved in decision‐making. This predicts that strengthening FPC theta oscillations via FPC tACS should enhance the functional coupling between FPC and the decision network during metacognitive judgments.
Stimulation effects on metacognitive accuracy. Compared with sham (A), high‐intensity theta tACS (B) but not high‐intensity alpha tACS (C) significantly impaired metacognitive accuracy, indicated by smaller differences between the slopes of logistic curves (which capture revealed decision uncertainty) for low and high confidence decisions. For illustration purposes (not for statistical analysis), we split data into low‐ and high‐confidence decisions. (D) Individual parameter estimates (extracted from the GLMM) for the confidence × DVsigned interaction, plotted separated for the tACS conditions. Black dots indicate individual data points. Asterisks indicate significant effects (*p < 0.05).
Neural correlates of (z‐transformed) confidence and choice difficulty in the sham baseline condition. (A) Confidence ratings negatively correlated with activation in the prefrontal and parietal cortex, including the bilateral FPC. (B) Activations in similar regions correlated with choice difficulty (low values for DVunsigned). Activation maps are thresholded at p < 0.001 uncorrected, minimum cluster size = 20 voxels. We found no significant tACS effects on the neural correlates of confidence or DVunsigned. (C) Overlap between neural correlates of decreasing confidence and choice difficulty. (D) FPC activation was more strongly related to decreasing confidence ratings than to choice difficulty. For illustration purposes, we show here individual parameter estimates extracted from the meta‐analysis FPC ROI (inset). Black dots indicate individual data points. Asterisks indicate significant effects in the second‐level analyses (*p < 0.05).
Stimulation effects on functional coupling with FPC. (A) Theta tACS significantly modulated the confidence‐related coupling of DLPFC with FPC (seed region) compared with sham and alpha tACS. (B) Parameters extracted from the significant DLPFC cluster suggest that under sham the FPC shows enhanced coupling with DLPFC for decreasing confidence ratings (i.e., difficult metacognitive judgments), and this enhanced coupling for decreasing confidence trials is reduced under theta tACS. Note that in this plot extracted parameters are for illustration purpose only, not for statistical inference. Asterisks indicate significant effects in the second‐level analyses (*p < 0.05). (C) Under sham, DLPFC coupling with the FPC seed region for decreasing confidence related to individual differences in metacognitive accuracy, and (D) stimulation effects on FPC‐DLPFC coupling correlated with the influence of theta tACS on the accuracy of metacognitive judgments: worse metacognitive skills (either under sham or as result of FPC tACS) were associated with stronger FPC‐DLPFC coupling for decreasing confidence.
Behavioral and imaging results for the mentalizing (false‐belief) task. (A) FPC theta or alpha tACS did not significantly affect performance (log‐transformed reaction times) in the mentalizing task. (B) Mentalizing demands (Switch>No‐switch)Jill > (Switch>No‐switch)self significantly correlated with activation in regions belonging to the mentalizing network, including precuneus and posterior temporal cortex. Activation maps are thresholded at p < 0.001 uncorrected, minimum cluster size = 20 voxel. We found no significant tACS effects on the neural correlates of mentalizing.
Frontopolar Cortex Interacts With Dorsolateral Prefrontal Cortex to Causally Guide Metacognition

January 2025

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90 Reads

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Marius Moisa

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Christian C. Ruff

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Alexander Soutschek
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Human Brain Mapping, part of Wiley's Influence Series, is a functional neuroanatomy and neuroimaging journal where all disciplines of neurology collide to advance the field. The journal offers basic, clinical, technical and theoretical research in the rapidly expanding field of human brain mapping. Proudly accessible, every issue is open to the world.

Recent articles


Temporal Interference Stimulation Boosts Working Memory Performance in the Frontoparietal Network
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  • Full-text available

February 2025

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6 Reads

Suwang Zheng

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Kun Huang

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Yu Liu

Temporal interference (TI) stimulation is a novel neuromodulation technique that overcomes the depth limitations of traditional transcranial electrical stimulation while avoiding the invasiveness of deep brain stimulation. Our previous behavioral research has demonstrated the effects of multi‐target TI stimulation in enhancing working memory (WM) performance, however, the neural mechanisms of this special form of envelope modulation remain unclear. To address this issue, here we designed this randomized, double‐blind, crossover study, which consisted of a task‐based functional magnetic resonance imaging (fMRI) experiment, to explore how offline TI stimulation modulated brain activity and behavioral performance in healthy adults. We conducted a 2 × 2 within‐subjects design with two factors: stimulation (TI vs. Sham) and time (pre vs. post). Participants received two stimulation protocols in a random order: TI (beat frequency: 6 Hz, targeting middle frontal gyrus [MFG] and inferior parietal lobule [IPL]) and sham stimulation. Neuroimaging data of a WM task with different cognitive loads were acquisited immediately before and after stimulation. We found TI stimulation significantly improved d′ in the high‐demand WM task. Whole‐brain analysis showed the significant time‐by‐stimulation interactions in two main clusters in IPL and precuneus with lower activation after TI stimulation. The generalized psychophysiological interaction (gPPI) analysis revealed a significant interaction in task‐modulated connectivity between MFG and IPL, with improvement observed after TI stimulation. Notably, this increasing functional connectivity induced by TI stimulation was positively correlated with better behavioral performance. Overall, our findings show specific effects of TI stimulation on brain activation and functional connectivity in the frontoparietal network and may contribute to provide new perspectives for future neuromodulation applications.


Brainstem segmentation performance. (a) The target structures mesencephalon (purple), pons (green) and medulla oblongata (blue) are shown for a patient with cerebral small vessel disease. (b) Dice similarity coefficient (DSC) for the overlap of inferred with ground truth masks in the three subregions and the total brainstem. Bottom panels zoom into the range above 0.85.
Technical validation. Bland–Altman plots for scan‐rescan repeatability (a) and inter‐scanner reproducibility (b) of total brainstem volume. Dashed lines indicate (from top to bottom) upper limit of agreement (LOA), constant bias, and lower LOA. The blue line indicates the proportional bias with confidence interval (grey).
Clinical Validation in MSA patients (n = 16). Percent volume change (PVC) over 1 year in the subregions and the total brainstem. Individual patients are shown as colored dots with the same color code across regions. The dashed red line indicates threshold for pathological brainstem atrophy at −0.37% per annum. One extreme outlier (PVC = +14.1%) observed in the medulla oblongata analysis using MD‐GRU is depicted with an arrow and was the result of a larger segmentation error on the baseline image.
Lesion filling. (a) Segmentation of the pons in the presence of an MS‐typical brainstem lesion without (middle) and with (right) lesion filling. (b) Lesioned volume as percentage of the total brainstem and its subregions. The number of patients with lesions in each region is shown above the boxplots. (c) Dice similarity coefficient (DSC) for the fit of the inferred with the ground truth brainstem segmentations before (none) and after filling lesions with FSL or ANTs. Lower panels show significance scores with ranks (numbers in the dots). (d) Coverage of lesions by the inferred brainstem mask as percentage of the lesion volume.
Extended Technical and Clinical Validation of Deep Learning‐Based Brainstem Segmentation for Application in Neurodegenerative Diseases

February 2025

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4 Reads

Benno Gesierich

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Laura Sander

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Lukas Pirpamer

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Marco Duering

Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. This study aimed to (1) optimize deep learning‐based brainstem segmentation for a wide range of pathologies and T1‐weighted image acquisition parameters, (2) conduct a systematic technical and clinical validation, (3) improve segmentation quality in the presence of brainstem lesions, and (4) make an optimized brainstem segmentation tool available for public use. An intentionally heterogeneous ground truth dataset (n = 257) was employed in the training of deep learning models based on multi‐dimensional gated recurrent units (MD‐GRU) or the nnU‐Net method. Segmentation performance was evaluated against ground truth labels. FreeSurfer was used for benchmarking in subsequent validation. Technical validation, including scan‐rescan repeatability (n = 46) and inter‐scanner reproducibility (n = 20, 3 different scanners) in unseen data, was conducted in patients with cerebral small vessel disease. Clinical validation in unseen data was performed in 1‐year follow‐up data of 16 patients with multiple system atrophy, evaluating the annual percentage volume change. Two lesion filling algorithms were investigated to improve segmentation performance in 23 patients with multiple sclerosis. The MD‐GRU and nnU‐Net models demonstrated very good segmentation performance (median Dice coefficients ≥ 0.95 each) and outperformed a previously published model trained on a narrower dataset. Scan–rescan repeatability and inter‐scanner reproducibility yielded similar Bland–Altman derived limits of agreement for longitudinal FreeSurfer (total brainstem volume repeatability/reproducibility 0.68/1.85), MD‐GRU (0.72/1.46), and nnU‐Net (0.48/1.52). All methods showed comparable performance in the detection of atrophy in the total brainstem (atrophy detected in 100% of patients) and its substructures. In patients with multiple sclerosis, lesion filling further improved the accuracy of brainstem segmentation. We enhanced and systematically validated two fully automated deep learning brainstem segmentation methods and released them publicly. This enables a broader evaluation of brainstem volume as a candidate biomarker for neurodegeneration.


Decoding Parametric Grip‐Force Anticipation From fMRI Data

February 2025

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18 Reads

Previous functional magnetic resonance imaging (fMRI) studies have shown that activity in premotor and parietal brain‐regions covaries with the intensity of upcoming grip‐force. However, it remains unclear how information about the intended grip‐force intensity is initially represented and subsequently transformed into a motor code before motor execution. In this fMRI study, we used multivoxel pattern analysis (MVPA) to decode where and when information about grip‐force intensities is parametrically coded in the brain. Human participants performed a delayed grip‐force task in which one of four cued levels of grip‐force intensity had to be maintained in working memory (WM) during a 9‐s delay‐period preceding motor execution. Using time‐resolved MVPA with a searchlight approach and support vector regression, we tested which brain regions exhibit multivariate WM codes of anticipated grip‐force intensities. During the early delay period, we observed above‐chance decoding in the ventromedial prefrontal cortex (vmPFC). During the late delay period, we found a network of action‐specific brain regions, including the bilateral intraparietal sulcus (IPS), left dorsal premotor cortex (l‐PMd), and supplementary motor areas. Additionally, cross‐regression decoding was employed to test for temporal generalization of activation patterns between early and late delay periods with those during cue presentation and motor execution. Cross‐regression decoding indicated temporal generalization to the cue period in the vmPFC and to motor‐execution in the l‐IPS and l‐PMd. Together, these findings suggest that the WM representation of grip‐force intensities undergoes a transformation where the vmPFC encodes information about the intended grip‐force, which is subsequently converted into a motor code in the l‐IPS and l‐PMd before execution.


Head Motion in Diffusion Magnetic Resonance Imaging: Quantification, Mitigation, and Structural Associations in Large, Cross‐Sectional Datasets Across the Lifespan

February 2025

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1 Read

Kurt G. Schilling

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Karthik Ramadass

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Viljami Sairanen

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Bennett A. Landman

Head motion during diffusion magnetic resonance imaging (MRI) scans can cause numerous artifacts and biases subsequent quantification. However, a thorough characterization of motion across multiple scans, cohorts, and consortiums has not been performed. To address this, we designed a study with three aims. First, we aimed to characterize subject motion across several large cohorts, utilizing 13 cohorts comprised of 16,995 imaging sessions (age 0.1–100 years, mean age = 63 years; 7220 females; 3175 cognitively impaired adults; 471 developmentally delayed children) to describe the magnitude and directions of subject movement. Second, we aimed to investigate whether state‐of‐the‐art diffusion preprocessing pipelines mitigate biases in quantitative measures of microstructure and connectivity by taking advantage of datasets with scan‐rescan acquisitions and ask whether there are detectable differences between the same subjects when scans and rescans have differing levels of motion. Third, we aimed to investigate whether there are structural connectivity differences between movers and non‐movers. We found that (1) subjects typically move 1–2 mm/min with most motion as translation in the anterior–posterior direction and rotation around the right–left axis; (2) Modern preprocessing pipelines can effectively mitigate motion to the point where biases are not detectable with current analysis techniques; and (3) There are no apparent differences in microstructure or macrostructural connections in participants who exhibit high motion versus those that exhibit low motion. Overall, characterizing motion magnitude and directions, as well as motion correlates, informs and improves motion mitigation strategies and image processing pipelines.


Age and gender distribution for infants with CHD and HC infants.
Detecting brain developmental abnormalities in infants with CHD by using the direct group‐level comparison (a) and brain normative modeling‐based method (b). HCs represent the healthy controls, and CHDs represent the infants with complex CHD. The overlapping regions are shown in (c). **pfdr<0.01$$ {p}_{\mathrm{fdr}}<0.01 $$ and ****pfdr<0.0001$$ {p}_{\mathrm{fdr}}<0.0001 $$.
Comparison results of developmental trajectories of regional surface area between CHD and HC infants. The 68 brain regions are classified into seven functional networks. SD and ID represent the differences in interaction effect (i.e., slope difference) and main effect (i.e., intercept difference), respectively. *pfdr<0.05$$ {p}_{\mathrm{fdr}}<0.05 $$, **pfdr<0.01$$ {p}_{\mathrm{fdr}}<0.01 $$, and ***pfdr<0.001$$ {p}_{\mathrm{fdr}}<0.001 $$.
Relationships among blood oxygen‐carrying capacity, cortical surface area, and gross motor skills. (a) Correlation results between regional surface area and blood oxygen‐carrying capacity; (b) correlation results between surface area and gross motor skills; (c) mediation analysis results between blood oxygen‐carrying capacity and gross motor skills via cortical surface area, where * indicates the significant effect.
Alteration in Cortical Structure Mediating the Impact of Blood Oxygen‐Carrying Capacity on Gross Motor Skills in Infants With Complex Congenital Heart Disease

February 2025

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5 Reads

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Pengcheng Xue

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Meijiao Zhu

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Daoqiang Zhang

Congenital heart disease (CHD) is the most common congenital anomaly, leading to an increased risk of neurodevelopmental abnormalities in many children with CHD. Understanding the neurological mechanisms behind these neurodevelopmental disorders is crucial for implementing early interventions and treatments. In this study, we recruited 83 infants aged 12–26.5 months with complex CHD, along with 86 healthy controls (HCs). We collected multimodal data to explore the abnormal patterns of cerebral cortex development and explored the complex interactions among blood oxygen‐carrying capacity, cortical development, and gross motor skills. We found that, compared to healthy infants, those with complex CHD exhibit significant reductions in cortical surface area development, particularly in the default mode network. Most of these developmentally abnormal brain regions are significantly correlated with the blood oxygen‐carrying capacity and gross motor skills of infants with CHD. Additionally, we further discovered that the blood oxygen‐carrying capacity of infants with CHD can indirectly predict their gross motor skills through cortical structures, with the left middle temporal area and left inferior temporal area showing the greatest mediation effects. This study identified biomarkers for neurodevelopmental disorders and highlighted blood oxygen‐carrying capacity as an indicator of motor development risk, offering new insights for the clinical management CHD.


Static and Dynamic Cross‐Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients

February 2025

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7 Reads

Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter‐network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 SZ patients and 160 demographically matched healthy controls (HC). Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available functional brain networks between SZ patients and HC. These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN), and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time‐varying connectivity strength across functional regions from each source network, compared to HC. C‐means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low‐scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large‐scale functional entropy correlation. K‐means clustering analysis on time‐indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that in HC, the brain primarily communicates through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. Individuals with SZ are significantly less likely to attain these more focused and structured transient connectivity patterns. The proposed ICE measure presents a novel framework for gaining deeper insight into mechanisms of healthy and diseased brain states and represents a useful step forward in developing advanced methods to help diagnose mental health conditions.


Methodology of connectivity and delays computation in considered modalities. Left: Source‐reconstructed MEG data are reported in top‐left. The data are z‐scored and thresholded. The probabilities are defined as the frequency with which region j went above the threshold after region i did. Finally, the delays are defined as the median time it took region j to go above the threshold after region i did. Right: Schematic representation of the CCEP processing. In the toy example reported, three stimulations have been delivered to parcel i and recorded from parcel j, via intracranial electrodes depicted here with dashed thick lines. The time courses represent z‐scored (with respect to prestimulus baseline) responses to stimulation. The transition probabilities (probabilistic connectivities) for the tract i, j are defined as the ratio of stimulations delivered in parcel i that elicited an above‐threshold response in the recording electrode in parcel j. In the toy example depicted in the figure, two out of the three stimulations elicited an above‐threshold response. Hence, the corresponding probability equals ⅔. With respect to delays, we have computed the median time it took from stimulation to the maximum of the first CCEP peak appearing after the threshold‐crossing in the recording electrode. In the toy example, the delays for the two pulses that reached the threshold correspond to t1 = 20 ms,  t2 = 22 ms, resulting in a median of 21 ms.
Comparison of results obtained from each modality. Top‐left: Connectivity probabilities as obtained from the Avalanche Transition Matrices (ATM). Source ROIs are in rows, and target ROIs are in columns. This convention remains valid for the remaining presented connectivity matrices. Top‐center: The matrix contains transition probabilities as obtained from the F‐TRACT dataset. Top‐right: Each dot of the scatterplot corresponds to a connection between two cortical parcels, the x‐axis informs about probabilities obtained using the ATMs, and the y‐axis informs about probabilities as obtained using F‐TRACT. The least‐squares fit line is also reported. To the far right, the distribution of the correlations was obtained by shuffling the temporal course of the ATMs (while preserving their spatial properties—see Section 4 for details). The green dashed line corresponds to the empirically observed correlation. Bottom‐left: The matrix contains the delays as obtained from the ATM dataset. Bottom‐center: The matrix contains the delays as obtained from F‐TRACT. Bottom‐right: Similarly as above, each dot of the scatterplot corresponds to a connection between two cortical ROIs, the x‐axis shows the delays obtained using the ATMs, and the y‐axis shows the delays obtained using F‐TRACT. The least‐squares line is also reported. To the far right, the distribution corresponds to the correlations obtained with the surrogate data, and the green dashed line marks the empirically observed correlation. Note that the F‐TRACT matrices shown in the second column were not masked for at least 50 trials per connection (see Section 4), but this mask was applied to obtain scatter plots shown in the right.
Intermodal consistency grouped by the parcel of signal propagation origin. To the left, per each region, we report the average correlation, across all the incident edges, between the transition probabilities computed from the F‐TRACT and those from the MEG dataset. To the right, the same plot for delays. Gray regions correspond to regions where the correlations did not reach statistical significance (defined as p < 0.05).
Intermodal Consistency of Whole‐Brain Connectivity and Signal Propagation Delays

February 2025

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17 Reads

Measuring propagation of perturbations across the human brain and their transmission delays is critical for network neuroscience, but it is a challenging problem that still requires advancement. Here, we compare results from a recently introduced, noninvasive technique of functional delays estimation from source‐reconstructed electro/magnetoencephalography, to the corresponding findings from a large dataset of cortico‐cortical evoked potentials estimated from intracerebral stimulations of patients suffering from pharmaco‐resistant epilepsies. The two methods yield significantly similar probabilistic connectivity maps and signal propagation delays, in both cases characterized with Pearson correlations greater than 0.5 (when grouping by stimulated parcel is applied for delays). This similarity suggests a correspondence between the mechanisms underpinning the propagation of spontaneously generated scale‐free perturbations (i.e., neuronal avalanches observed in resting state activity studied using magnetoencephalography) and the spreading of cortico‐cortical evoked potentials. This manuscript provides evidence for the accuracy of the estimate of functional delays obtained noninvasively from reconstructed sources. Conversely, our findings show that estimates obtained from externally induced perturbations in patients capture physiological activities in healthy subjects. In conclusion, this manuscript constitutes a mutual validation between two modalities, broadening their scope of applicability and interpretation. Importantly, the capability to measure delays noninvasively (as per MEG) paves the way for the inclusion of functional delays in personalized large‐scale brain models as well as in diagnostic and prognostic algorithms.


Relating Functional Connectivity and Alcohol Use Disorder: A Systematic Review and Derivation of Relevance Maps for Regions and Connections

Alcohol Use Disorder (AUD), a prevalent and potentially severe psychiatric condition, is one of the leading causes of morbidity and mortality. This systematic review investigates the relationship between AUD and resting‐state functional connectivity (rsFC) derived from functional magnetic resonance imaging data. Following the PRISMA guidelines, a comprehensive search yielded 248 papers, and a screening process identified 39 studies with 73 relevant analyses. Using the automated anatomical labeling atlas for whole‐brain parcellation, relevance maps were generated to quantify associations between brain regions and their connections with AUD. These outcomes are based on the frequency with which significant findings are reported in the literature, to deal with the challenge of methodological diversity between analyses, including sample sizes, types of independent rsFC features, and AUD measures. The analysis focuses on whole‐brain studies to mitigate selection biases associated with seed‐based approaches. The most frequently reported regions include the middle and superior frontal gyri, the anterior cingulate cortex, and the insula. The generated relevance maps can serve as a valuable tool for formulating hypotheses and advancing our understanding of AUD's neural correlates in the future. This work also provides a template on how to quantitatively summarize a diverse literature, which could be applied to more specific aspects of AUD, including craving, relapse, binge drinking, or other diseases.


Attenuation of High Gamma Activity by Repetitive Motor Tasks

February 2025

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28 Reads

High gamma activity (HGA) is a crucial biomarker for functional brain mapping, particularly in sensorimotor areas, to preserve functionality after brain surgeries. HGA mapping paradigms typically involve multiple task blocks alternating with resting (R) conditions, where each block comprises consecutive tasks under nonresting (NR) conditions. However, the repetitive nature of these tasks may lead to attenuation due to repetition suppression, potentially compromising the accuracy of HGA mapping. This study tests the hypothesis that repetitive grasping paradigms result in attenuated HGA over time in sensorimotor areas. It explores the temporal and spatial characteristics of this attenuation to optimize electrocorticography (ECoG) HGA protocols and enhance result interpretation. Eleven consecutive patients who underwent surgical treatment of intractable epilepsy or malignant glioma were included in this study. Intracranial electrode locations on the pre‐ and postcentral gyrus were considered regions of interest (ROI). Each patient performed ten blocks of ten consecutive grasping trials. The mean z‐scored HGA (60–170 Hz) across these trials was calculated, and attenuation was analyzed using the Kruskal–Wallis test. Obtained signals were also divided into three grouped periods for R and NR groups to assess short‐term attenuation within movement blocks and long‐term attenuation over multiple blocks. Electrode locations were mapped to the MNI152 (Montreal Neurological Institute) brain template to investigate the spatial distribution of attenuation. Distances from each electrode to the hand‐knob region were compared between attenuated and nonattenuated electrodes. A total of 568 electrodes from 11 patients were analyzed, including 139 electrodes within the ROI. Thus, 60 electrodes demonstrated significant HGAs during the grasping task (p < 0.05). Sensorimotor HGA z‐scores significantly attenuated over time during both consecutive grasping trials and repeated blocks. Short‐term attenuation (25%, 15/60 electrodes in ROI) was more pronounced than long‐term attenuation (15%, 9/60 electrodes in ROI). Notably, three patients undergoing intraoperative mapping demonstrated less short‐term attenuation compared to long‐term attenuation. Spatially, attenuated electrodes clustered around the hand‐knob region of the precentral gyrus and adjacent areas of the postcentral gyrus. However, no significant differences were observed in the distances from electrodes to the hand‐knob region between attenuated and nonattenuated electrodes. The present study showed that repetitive grasping tasks attenuated the HGA of significant electrodes in the sensorimotor area over time. Considering the findings with the characteristics can further improve the usability of ECoG mapping in terms of more precise results in the most reasonable mapping time.


MRE imaging protocol starts with mechanical actuation shown on the far left. Displacements (shown in X, Z, and Y directions, respectively) due to the induced vibrations are captured using a custom imaging sequence with motion encoding gradients. A nonlinear inversion algorithm is then used to solve for the mechanical properties of the tissue and create whole brain property maps.
(A) Regions of interest overlayed on MRE stiffness maps next to the corresponding anatomical image. (B) Representative plots showing the relationship between stiffness and age cross‐sectionally across the study population for the regions displayed above. Participants are denoted by individual points with the best fit line indicating average yearly stiffness loss plotted as the overlayed dash line. In the upper right corner is displayed the significance of the correlation between age and stiffness (p), the correlation coefficient of the age and stiffness relationship (r), and slope of the stiffness and age relationship in kPa/year (s).
(A) Average shear stiffness for each bilateral cortical region was evaluated at 75 years old. (B) Average stiffness loss in kPa/year for each cortical region exhibiting a significant relationship with age; greater stiffness loss per year is represented by warmer colors. The brain graphics were created with the Simple Brain Plot Toolbox (Scholtens, de Lange, and van den Heuvel 2021).
Regions exhibiting significant cortical thinning with age. Greater age‐related differences are represented by warmer colors.
For visualization, plots illustrating significant relationships between neuroticism and damping ratio in the rostral middle frontal cortex and precentral gyrus. Each plot includes all participants plotted as points with a line of best fit overlayed as the dashed line. The Bonferroni corrected p‐value and standardized β are displayed in the upper left corner. These regions are shown anatomically in the bottom right with the color of each region indicating the magnitude of the standardized β, where brighter green indicates a stronger relationship between the regional damping ratio and the neuroticism score).
Mechanical Properties of the Cortex in Older Adults and Relationships With Personality Traits

February 2025

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14 Reads

Aging and neurodegeneration impact structural brain integrity and can result in changes to behavior and cognition. Personality, a relatively stable trait in adults as compared to behavior, in part relies on normative individual differences in cellular organization of the cerebral cortex, but links between brain structure and personality expression have been mixed. One key finding is that personality has been shown to be a risk factor in the development of Alzheimer's disease, highlighting a structure–trait relationship. Magnetic resonance elastography (MRE) has been used to noninvasively study age‐related changes in tissue mechanical properties because of its high sensitivity to both the microstructural health and the structure–function relationship of the tissue. Recent advancements in MRE methodology have allowed for reliable property recovery of cortical subregions, which had previously presented challenges due to the complex geometry and overall thin structure. This study aimed to quantify age‐related changes in cortical mechanical properties and the relationship of these properties to measures of personality in an older adult population (N = 57; age 60–85 years) for the first time. Mechanical properties including shear stiffness and damping ratio were calculated for 30 bilateral regions of the cortex across all four lobes, and the NEO Personality Inventory (NEO‐PI) was used to measure neuroticism and conscientiousness in all participants. Shear stiffness and damping ratio were found to vary widely across regions of the cortex, upward of 1 kPa in stiffness and by 0.3 in damping ratio. Shear stiffness changed regionally with age, with some regions experiencing accelerated degradation compared to neighboring regions. Greater neuroticism (i.e., the tendency to experience negative emotions and vulnerability to stress) was associated with high damping ratio, indicative of poorer tissue integrity, in the rostral middle frontal cortex and the precentral gyrus. This study provides evidence of structure–trait correlates between physical mechanical properties and measures of personality in older adults and adds to the supporting literature that neurotic traits may impact brain health in cognitively normal aging.


Grey‐Matter Structure Markers of Alzheimer's Disease, Alzheimer's Conversion, Functioning and Cognition: A Meta‐Analysis Across 11 Cohorts

February 2025

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28 Reads

Alzheimer's disease (AD) brain markers are needed to select people with early‐stage AD for clinical trials and as quantitative endpoint measures in trials. Using 10 clinical cohorts (N = 9140) and the community volunteer UK Biobank (N = 37,664) we performed region of interest (ROI) and vertex‐wise analyses of grey‐matter structure (thickness, surface area and volume). We identified 94 trait‐ROI significant associations, and 307 distinct cluster of vertex‐associations, which partly overlap the ROI associations. For AD versus controls, smaller hippocampus, amygdala and of the medial temporal lobe (fusiform and parahippocampal gyri) was confirmed and the vertex‐wise results provided unprecedented localisation of some of the associated region. We replicated AD associated differences in several subcortical (putamen, accumbens) and cortical regions (inferior parietal, postcentral, middle temporal, transverse temporal, inferior temporal, paracentral, superior frontal). These grey‐matter regions and their relative effect sizes can help refine our understanding of the brain regions that may drive or precede the widespread brain atrophy observed in AD. An AD grey‐matter score evaluated in independent cohorts was significantly associated with cognition, MCI status, AD conversion (progression from cognitively normal or MCI to AD), genetic risk, and tau concentration in individuals with none or mild cognitive impairments (AUC in 0.54–0.70, p‐value < 5e‐4). In addition, some of the grey‐matter regions associated with cognitive impairment, progression to AD (‘conversion’), and cognition/functional scores were also associated with AD, which sheds light on the grey‐matter markers of disease stages, and their relationship with cognitive or functional impairment. Our multi‐cohort approach provides robust and fine‐grained maps the grey‐matter structures associated with AD, symptoms, and progression, and calls for even larger initiatives to unveil the full complexity of grey‐matter structure in AD.


Pseudo‐MRI Engine for MRI‐Free Electromagnetic Source Imaging

February 2025

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22 Reads

Structural head MRIs are a crucial ingredient in MEG/EEG source imaging; they are used to define a realistically shaped volume conductor model, constrain the source space, and visualize the source estimates. However, individual MRIs are not always available, or they may be of insufficient quality for segmentation, leading to the use of a generic template MRI, matched MRI, or the application of a spherical conductor model. Such approaches deviate the model geometry from the true head structure and limit the accuracy of the forward solution. Here, we implemented an easy‐to‐use tool, pseudo‐MRI engine, which utilizes the head‐shape digitization acquired during a MEG/EEG measurement for warping an MRI template to fit the subject's head. To this end, the algorithm first removes outlier digitization points, densifies the point cloud by interpolation if needed, and finally warps the template MRI and its segmented surfaces to the individual head shape using the thin‐plate‐spline method. To validate the approach, we compared the geometry of segmented head surfaces, cortical surfaces, and canonical brain regions in the real and pseudo‐MRIs of 25 subjects. We also tested the MEG source reconstruction accuracy with pseudo‐MRIs against that obtained with the real MRIs from individual subjects with simulated and real MEG data. We found that the pseudo‐MRI enables comparable source localization accuracy to the one obtained with the subject's real MRI. The study indicates that pseudo‐MRI can replace the need for individual MRI scans in MEG/EEG source imaging for applications that do not require subcentimeter spatial accuracy.


A Statistical Characterization of Dynamic Brain Functional Connectivity

February 2025

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36 Reads

This study examined the statistical underpinnings of dynamic functional connectivity in mental disorders, using resting‐state fMRI signals. Notably, there has been an absence of research demonstrating the non‐stationarity of the empirical probability distribution of functional connectivity. This gap has prompted debate on the existence of dynamic functional connectivity, leading skeptics to question its relevance and the reliability of research findings. Our aim was to fill this gap by conducting a comprehensive empirical distribution analysis of functional connectivity, using Pearson's correlation as a measure. We conducted our analysis on a set of preprocessed resting‐state fMRI samples obtained from 186 subjects selected from the UCLA Consortium for Neuropsychiatric Phenomics dataset. Departing from conventional methods that aggregated signals over voxels within a region of interest, our approach leveraged individual voxel signals. Specifically, our approach offered a precise characterization of the empirical probability distribution of resting‐state fMRI signals by evaluating the temporal variations and non‐stationarity in dynamic functional connectivity, as measured by Pearson's correlation. Our study investigated functional connectivity patterns across 49 regions of interest, comparing healthy control subjects with patients diagnosed with ADHD, bipolar disorder, and schizophrenia. Our analysis revealed that (1) the empirical distribution of the correlation coefficient exhibited non‐stationarity, (2) the beta distribution was an accurate approximation of the exact correlation coefficient distribution, and (3) the empirical distribution of means derived from the fitted beta distributions, unraveled distinctive dynamic functional connectivity patterns with potential as biomarkers associated with different mental disorders. A key contribution of our study was the presentation of the first comprehensive empirical distribution analysis of dynamic functional connectivity, thus providing compelling evidence for its existence. Overall, our study presented an innovative statistical approach that advances our understanding of the dynamic nature of functional connectivity patterns derived from resting‐state fMRI. Our examination of the empirical distribution of dynamic functional connectivity provided solid evidence supporting its existence. The distinctive dynamic functional connectivity patterns we identified across various mental disorders hold promise as potential biomarkers for further development.


Analysis workflow. Resting‐state fMRI data were acquired for each subject during five runs differing in TR (TR = 0.5 s, 905 volumes; TR = 0.7 s, 646 volumes; TR = 1 s, 452 volumes; TR = 2 s, 226 volumes; TR = 3 s, 150 volumes). After preprocessing, parcellation was performed on the basis of an atlas combining Glasser parcellation (Glasser et al. 2016) and a subcortical parcellation provided by the Human Connectome Project. Two fingerprinting analyses were performed. Within‐TR analysis: After parcellation, for each TR separately, time course was split in two halves, and two sets of functional connectivity (FC) matrices (test and retest) were computed as input for the group‐level principal component analysis (PCA). FC matrices were then reconstructed using the optimal number of PCA components. Between‐TR analysis: After parcellation, for each TR pairwise comparison, the whole time course from two different TR runs (test and retest) was used to compute FC matrices. The optimal number of components resulting from the group‐level PCA were used to reconstruct back each FC matrix. For both the within‐TR and the between‐TR analysis, Pearson's correlation coefficients were computed between test and retest sets of FC matrices, in order to create an identifiability matrix for each TR condition and TR pairwise comparison. Finally, edgewise intraclass correlation (ICC) was computed in two different ways, resulting in a subject and a TR ICC. The whole fingerprinting analysis was repeated using all volumes and only the first 150 volumes of each TR run. CSF, cerebrospinal fluid; FC, functional connectivity; ICC, intraclass correlation; PCA, principal component analysis; WM, white matter. *Glasser et al. (2016); **Human Connectome Project, release Q3.
Within‐TR fingerprinting. (A) Identifiability matrices of PCA‐reconstructed functional connectivity profiles, extracted from the whole scanning time course, separately for each TR run. (B) Box plots of differential identifiability (Idiff), self‐identifiability (Iself), and others‐identifiability (Iothers) distributions computed at a subject‐level after PCA‐reconstruction, when using all (white) and 150 (gray) volumes for the fingerprinting analysis.
Between‐TR fingerprinting. (A) Identifiability matrices of the PCA‐reconstructed functional connectivity profiles, extracted from the whole scanning time course, separately for each TR combination. (B) Confusion matrices of the differential identifiability (Idiff) values computed from the between‐TR identifiability matrices (A). (C) Confusion matrices of the success rate (SR) values computed from the between‐TR identifiability matrices (A).
Edgewise intra‐class correlation (ICC) analysis of subject identifiability and task identifiability. (A, B) Edgewise subject (A) and TR ICC (B) matrices, showing only functional connections with ICC values significantly higher than the mean distribution (i.e., lying in the 95th percentile). The brain regions are ordered according to Yeo's (Yeo et al. 2011) functional resting state networks (FNs): visual (VIS), somato‐motor (SM), dorsal attention (DA), ventral attention (VA), limbic system (L), fronto‐parietal (FP), default mode network (DMN), and subcortical regions (SUB). The colored dots refer to within FNs networks edges, while gray dots refer to between FNs networks edges, as in Amico and Goñi (2018). (C, D) Violin plots of edgewise subject (C) and TR (D) ICC distributions for the five FNs with the highest mean ICC value. Each colored violin plot indicates a different within FN, while gray violin plots indicate between FNs ICC distributions. The horizontal solid black line within each violin plot indicates the mean value of each distribution; the solid red line across the violin plots, instead, indicates the whole‐brain mean ICC value, as in Amico and Goñi (2018). The most prominent FNs for subject's identifiability (C) resulted: SUB, DMN, and the DMN‐SM, VA‐DMN, VA‐SM interactions. For TR identifiability (D) the most relevant FNs were: FP, DA, SUB, VIS and the VIS‐DA interactions. (E, F) Brain renders of nodal ICC, computed as the column‐wise mean of the edgewise ICC matrices for both subject (E) and TR (F) ICC, and represented at 5th–95th percentile threshold. Nodal ICC gives an assessment of the overall prominence of each brain region for subject's and TR identifiability. All plots refer to the all volumes analysis. Brain renders were created by running the Matlab code available in the Surface projection GitHub repository (https://github.com/rudyvdbrink/Surface_projection, Alexander‐Bloch et al. 2018).
TR(Acking) Individuals Down: Exploring the Effect of Temporal Resolution in Resting‐State Functional MRI Fingerprinting

January 2025

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56 Reads

Functional brain fingerprinting has emerged as an influential tool to quantify reliability in neuroimaging studies and to identify cognitive biomarkers in both healthy and clinical populations. Recent studies have revealed that brain fingerprints reside in the timescale‐specific functional connectivity of particular brain regions. However, the impact of the acquisition's temporal resolution on fingerprinting remains unclear. In this study, we examine for the first time the reliability of functional fingerprinting derived from resting‐state functional MRI (rs‐fMRI) with different whole‐brain temporal resolutions (TR = 0.5, 0.7, 1, 2, and 3 s) in a cohort of 20 healthy volunteers. Our findings indicate that subject identifiability within a fixed TR is successful across different temporal resolutions, with the highest identifiability observed at TR 0.5 and 3 s (TR(s)/identifiability(%): 0.5/64; 0.7/47; 1/44; 2/44; 3/56). We discuss this observation in terms of protocol‐specific effects of physiological noise aliasing. We further show that, irrespective of TR, associative brain areas make an higher contribution to subject identifiability (functional connections with highest mean ICC: within subcortical network [SUB; ICC = 0.0387], within default mode network [DMN; ICC = 0.0058]; between DMN and somato‐motor [SM] network [ICC = 0.0013]; between ventral attention network [VA] and DMN [ICC = 0.0008]; between VA and SM [ICC = 0.0007]), whereas sensory‐motor regions become more influential when integrating data from different TRs (functional connections with highest mean ICC: within fronto‐parietal network [ICC = 0.382], within dorsal attention network [DA; ICC = 0.373]; within SUB [ICC = 0.367]; between visual network [VIS] and DA [ICC = 0.362]; within VIS [ICC = 0.358]). We conclude that functional connectivity fingerprinting derived from rs‐fMRI holds significant potential for multicentric studies also employing protocols with different temporal resolutions. However, it remains crucial to consider fMRI signal's sampling rate differences in subject identifiability between data samples, in order to improve reliability and generalizability of both whole‐brain and specific functional networks' results. These findings contribute to a better understanding of the practical application of functional connectivity fingerprinting, and its implications for future neuroimaging research.


Decoding in the Fourth Dimension: Classification of Temporal Patterns and Their Generalization Across Locations

January 2025

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35 Reads

Neuroimaging research has increasingly used decoding techniques, in which multivariate statistical methods identify patterns in neural data that allow the classification of experimental conditions or participant groups. Typically, the features used for decoding are spatial in nature, including voxel patterns and electrode locations. However, the strength of many neurophysiological recording techniques such as electroencephalography or magnetoencephalography is in their rich temporal, rather than spatial, content. The present report introduces the time‐GAL toolbox, which implements a decoding method based on time information in electrophysiological recordings. The toolbox first quantifies the decodable information contained in neural time series. This information is then used in a subsequent step, generalization across location (GAL), which characterizes the relationship between sensor locations based on their ability to cross‐decode. Two datasets are used to demonstrate the usage of the toolbox, involving (1) event‐related potentials in response to affective pictures and (2) steady‐state visual evoked potentials in response to aversively conditioned grating stimuli. In both cases, experimental conditions were successfully decoded based on the temporal features contained in the neural time series. Spatial cross‐decoding occurred in regions known to be involved in visual and affective processing. We conclude that the approach implemented in the time‐GAL toolbox holds promise for analyzing neural time series from a wide range of paradigms and measurement domains providing an assumption‐free method to quantifying differences in temporal patterns of neural information processing and whether these patterns are shared across sensor locations.


Simplified schematic depiction of entorhinal cortex and hippocampal subfields circuitry (modified from Axmacher et al. 2006).
Visual representation of hippocampal subfields segmentation. (A) Sample coronal slices of hippocampal subfields in an anatomical T1‐ and T2‐weighted image from subject 03, from anterior (left) to posterior (right). (B) Sample sagittal slices of hippocampal subfields in an anatomical T1‐ and T2‐weighted image from subject 03, from medial (left) to lateral (right). Top row: T1 image. Second row: T2 image. Third row: Segmentation computed with T1 and T2 scans simultaneously, overlaid on the T2 images. Fourth row: Longitudinal segmentation computed with T1 and T2 scans simultaneously, overlaid on the T2 images.
Schematic of pattern separation and pattern completion analyses. (A) Pearson correlation coefficient (R) was calculated between successive sliding windows to assess temporal similarity in Regions A and B. R values were transformed into Fisher Z‐scores. (B) The distribution of temporal similarity within hippocampal pairs (ROIA–ROIB) for all window sizes (ranging from 15 to 50 TR). For each size, the pattern separation ratio value was computed as the proportion of sliding windows where Region A exhibited greater temporal similarity than Region B (ZA > ZB) relative to the total number of adjacent windows. Conversely, the pattern completion ratio represented the ratio of sliding windows where Region A exhibited lower temporal similarity than Region B (ZA < ZB) to the total number of adjacent windows. Nonparametric permutation tests (1000 times) were then conducted to evaluate the significance of the pattern separation (or completion) ratio at this window size. (C) The run‐level pattern separation (or completion) for each movie run. It was measured as the area between pattern separation (or completion) ratio values and the dividing line (y = 0.5) across all window sizes. The x‐axis corresponds to window size, and the y‐axis represented the ratio values of pattern separation (or completion). PC, pattern completion; PS, pattern separation.
Pattern separation and pattern completion analyses within the hippocampal (A) DG‐CA3, (B) CA3‐CA1, and (C) CA1‐SUB pairs during naturalistic stimuli in an example of subject 03 run 1. Three area graphs (left) presented the run‐level pattern separation or completion, which was measured as the area between the curve of pattern separation or completion ratio values (ranging from 15 to 50 TR) and the dividing line (y = 0.5). Three scatter diagrams (right) presented the distribution of adjacent sliding windows for pattern separation and completion within the corresponding hippocampal pair at one window size. Each dot represents the temporal similarity in the current hippocampal pair under a sliding window (50 TR), and then the pattern separation or completion ratio was calculated at each preselected given window size. (D) Pattern separation and pattern completion results in the whole hippocampal subfield. A two‐tailed one‐sample t‐test was performed against the chance level. Data plotted as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, Bonferroni corrected. PC, pattern completion; PS, pattern separation.
The dynamic changes of pattern separation and pattern completion processes within the DG‐CA3 (left), CA3‐CA1 (middle), and CA1‐SUB pairs (right) during (A) the first half, (B) the second half, and (C) the whole of the audio movie. The x‐axis represents the sequence of movie runs, while the y‐axis represents the value of Δ pattern separation‐pattern completion. Each dot represents a subject.
Pattern Separation and Pattern Completion Within the Hippocampal Circuit During Naturalistic Stimuli

January 2025

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13 Reads

Pattern separation and pattern completion in the hippocampus play a critical role in episodic learning and memory. However, there is limited empirical evidence supporting the role of the hippocampal circuit in these processes during complex continuous experiences. In this study, we analyzed high‐resolution fMRI data from the “Forrest Gump” open‐access dataset (16 participants) using a sliding‐window temporal autocorrelation approach to investigate whether the canonical hippocampal circuit (DG‐CA3‐CA1‐SUB) shows evidence consistent with the occurrence of pattern separation or pattern completion during a naturalistic audio movie task. Our results revealed that when processing continuous naturalistic stimuli, the DG‐CA3 pair exhibited evidence consistent with the occurrence of the pattern separation process, whereas both the CA3‐CA1 and CA1‐SUB pairs showed evidence consistent with pattern completion. Moreover, during the latter half of the audio movie, we observed evidence consistent with a reduction in pattern completion in the CA3‐CA1 pair and an increase in pattern completion in the CA1‐SUB pair. Overall, these findings improve our understanding of the evidence related to the occurrence of pattern separation and pattern completion processes during natural experiences.


Task design and stimulation setup. (A) In the confidence accuracy task, participants chose between an immediately available smaller‐sooner reward (e.g., 5 CHF today) and a larger‐later reward (e.g., 10 CHF in 20 days). After each decision, they were asked to indicate their confidence in having made the best choice on a scale from 0 to 7. (B) In the mentalizing (false‐belief) task, participants first viewed two consecutive images: in the first image, a person named Jack was holding a ball above one of two hats, while another person named Jill was watching. In the second image, Jack placed the ball in one of the two hats while Jill was absent. Participants had to indicate the position of the ball from either their own or Jill's perspective. Jill held either a true belief (position did not switch) or a false belief (position switched). Participants performed the confidence accuracy and mentalizing tasks in the MRI scanner in an interleaved way. (C) During task performance in the scanner, participants received theta (5 Hz), alpha (10 Hz), or sham tACS using a 3 × 1 electrode setup over the FPC. We estimated the electric field density (normE = volts per meter, V/m) with Simnibs 2.1 (current strength of 4 mA at central electrode). Warmer colors indicate higher electric field density. (D) According to two‐layer accounts of metacognition, the FPC enables accurate metacognitive judgments by reading‐out decision‐related information from other brain regions involved in decision‐making. This predicts that strengthening FPC theta oscillations via FPC tACS should enhance the functional coupling between FPC and the decision network during metacognitive judgments.
Stimulation effects on metacognitive accuracy. Compared with sham (A), high‐intensity theta tACS (B) but not high‐intensity alpha tACS (C) significantly impaired metacognitive accuracy, indicated by smaller differences between the slopes of logistic curves (which capture revealed decision uncertainty) for low and high confidence decisions. For illustration purposes (not for statistical analysis), we split data into low‐ and high‐confidence decisions. (D) Individual parameter estimates (extracted from the GLMM) for the confidence × DVsigned interaction, plotted separated for the tACS conditions. Black dots indicate individual data points. Asterisks indicate significant effects (*p < 0.05).
Neural correlates of (z‐transformed) confidence and choice difficulty in the sham baseline condition. (A) Confidence ratings negatively correlated with activation in the prefrontal and parietal cortex, including the bilateral FPC. (B) Activations in similar regions correlated with choice difficulty (low values for DVunsigned). Activation maps are thresholded at p < 0.001 uncorrected, minimum cluster size = 20 voxels. We found no significant tACS effects on the neural correlates of confidence or DVunsigned. (C) Overlap between neural correlates of decreasing confidence and choice difficulty. (D) FPC activation was more strongly related to decreasing confidence ratings than to choice difficulty. For illustration purposes, we show here individual parameter estimates extracted from the meta‐analysis FPC ROI (inset). Black dots indicate individual data points. Asterisks indicate significant effects in the second‐level analyses (*p < 0.05).
Stimulation effects on functional coupling with FPC. (A) Theta tACS significantly modulated the confidence‐related coupling of DLPFC with FPC (seed region) compared with sham and alpha tACS. (B) Parameters extracted from the significant DLPFC cluster suggest that under sham the FPC shows enhanced coupling with DLPFC for decreasing confidence ratings (i.e., difficult metacognitive judgments), and this enhanced coupling for decreasing confidence trials is reduced under theta tACS. Note that in this plot extracted parameters are for illustration purpose only, not for statistical inference. Asterisks indicate significant effects in the second‐level analyses (*p < 0.05). (C) Under sham, DLPFC coupling with the FPC seed region for decreasing confidence related to individual differences in metacognitive accuracy, and (D) stimulation effects on FPC‐DLPFC coupling correlated with the influence of theta tACS on the accuracy of metacognitive judgments: worse metacognitive skills (either under sham or as result of FPC tACS) were associated with stronger FPC‐DLPFC coupling for decreasing confidence.
Behavioral and imaging results for the mentalizing (false‐belief) task. (A) FPC theta or alpha tACS did not significantly affect performance (log‐transformed reaction times) in the mentalizing task. (B) Mentalizing demands (Switch>No‐switch)Jill > (Switch>No‐switch)self significantly correlated with activation in regions belonging to the mentalizing network, including precuneus and posterior temporal cortex. Activation maps are thresholded at p < 0.001 uncorrected, minimum cluster size = 20 voxel. We found no significant tACS effects on the neural correlates of mentalizing.
Frontopolar Cortex Interacts With Dorsolateral Prefrontal Cortex to Causally Guide Metacognition

January 2025

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90 Reads

Accurate metacognitive judgments about an individual's performance in a mental task require the brain to have access to representations of the quality and difficulty of first‐order cognitive processes. However, little is known about how accurate metacognitive judgments are implemented in the brain. Here, we combine brain stimulation with functional neuroimaging to determine the neural and psychological mechanisms underlying the frontopolar cortex's (FPC) role in metacognition. Specifically, we evaluate two‐layer neural architectures positing that FPC enables metacognitive judgments by communicating with brain regions encoding first‐order decision difficulty. In support of two‐layer architectures of metacognition, we found that high‐intensity transcranial alternating current stimulation (tACS; 4 mA peak‐to‐peak) over FPC impaired metacognitive accuracy; at the neural level, this impairment was reflected by reduced coupling between FPC and dorsolateral prefrontal cortex (DLPFC), particularly during difficult metacognitive judgments. We also evaluated conceptual accounts assuming that metacognition relies on self‐directed mentalizing. However, we observed no influence of FPC tACS on mentalizing performance and only a weak overlap of the networks underlying metacognition and mentalizing. Together, our findings put the FPC at the center of a two‐layer architecture that enables accurate evaluations of cognitive processes, mainly via the FPC's connectivity with regions encoding first‐level task difficulty, with little contributions from mentalizing‐related processes.


Correlations between markers of neurodegeneration. (A) Pearson correlations for comparisons between various markers of neurodegeneration are presented (Brain age gap calculated via Deep Brain Net (DBN), brain age gap calculated via BrainAgeR, hippocampal volume, and cortical thickness), stratified by racial identity, for both unimpaired (CDR 0) (A) and impaired (CDR > 0) (B) participants. The strongest correlation for unimpaired and impaired was between markers of Brain Age Gap. Relationships were generally consistent between the unimpaired and impaired for the various markers of neurodegeneration.
Neurodegenerative markers stratified by racial identity and Clinical Dementia Rating (CDR). *indicates padj < 0.05, **indicates padj < 0.001, ***indicates padj < 0.0001. Within CDR differences are indicated in grey. Within the ethno‐racial group differences are shown in their affiliated color (MA in pink, NHB in blue, NHW in yellow). For readability, differences spanning CDR and ethnoracial groups (e.g., CDR = 0 MA vs. CDR = 0.5 NHW) are omitted. (A) Brain Age Gap as calculated by Deep Brain Net increases with increasing CDR, indicating that brains appear older than chronological age in individuals with dementia. (B) Hippocampal volume declines with increasing CDR. Note that for MA, hippocampal volume does not decline until CDR ≥ 1. (C) Cortical thickness declines at the group level for MA and NHW, but not NHB.
Probability of CDR as a function of neurodegenerative marker, racial identity, and gender. Mexican American (MA) participants served as the reference cohort in these analyses and, as such, are not depicted in the forest plots. (A) There are significant effects of gender, brain age gap (BAG) as calculated by Deep Brain Net (DBN), and a significant interaction between BAG and racial identity for non‐Hispanic Whites (NHW) indicating that (B) the probability of being cognitively normal (CDR = 0) declines at a greater rate with increasing BAG for NHW than either Mexican American (MA) or non‐Hispanic Black (NHB) participants. (C) There are significant effects of gender, hippocampal volume, and racial identity for NHW and significant interactions between hippocampal volume and racial identity for both NHB and NHW, indicating that (D) the probability of different CDR diagnoses is least associated with hippocampal volume for MA. The probability of being CDR > 0 increases more rapidly for NHB than MA with declining hippocampal volume. This probability increases even more rapidly for NHW relative to MA. (E) There are significant effects of gender and cortical thickness, but not racial identity, on the probability of being cognitively normal or impaired. (F) This means that although the probability of CDR diagnosis changes with decreasing cortical thickness, there are no differences by ethnoracial identity.
Participant characteristics.
Cross‐Sectional Comparison of Structural MRI Markers of Impairment in a Diverse Cohort of Older Adults

January 2025

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23 Reads

Neurodegeneration is presumed to be the pathological process measure most proximal to clinical symptom onset in Alzheimer Disease (AD). Structural MRI is routinely collected in research and clinical trial settings. Several quantitative MRI‐based measures of atrophy have been proposed, but their low correspondence with each other has been previously documented. The purpose of this study was to identify which commonly used structural MRI measure (hippocampal volume, cortical thickness in AD signature regions, or brain age gap [BAG]) had the best correspondence with the Clinical Dementia Rating (CDR) in an ethno‐racially diverse sample. 2870 individuals recruited by the Healthy and Aging Brain Study—Health Disparities completed both structural MRI and CDR evaluation. Of these, 1887 individuals were matched on ethno‐racial identity (Mexican American [MA], non‐Hispanic Black [NHB], and non‐Hispanic White [NHW]) and CDR (27% CDR > 0). We estimated brain age using two pipelines (DeepBrainNet, BrainAgeR) and then calculated BAG as the difference between the estimated brain age and chronological age. We also quantified their hippocampal volumes using HippoDeep and cortical thicknesses (both an AD‐specific signature and average whole brain) using FreeSurfer. We used ordinal regression to evaluate associations between neuroimaging measures and CDR and to test whether these associations differed between ethno‐racial groups. Higher BAG (pDeepBrainNet = 0.0002; pBrainAgeR = 0.00117) and lower hippocampal volume (p = 0.0015) and cortical thickness (p < 0.0001) were associated with worse clinical status (higher CDR). AD signature cortical thickness had the strongest relationship with CDR (AICDeepBrainNet = 2623, AICwhole cortex = 2588, AICBrainAgeR = 2533, AICHippocampus = 2293, AICSignature Cortical Thickness = 1903). The relationship between CDR and atrophy measures differed between ethno‐racial groups for both BAG estimates and hippocampal volume, but not for cortical thickness. We interpret the lack of an interaction between ethno‐racial identity and AD signature cortical thickness on CDR as evidence that cortical thickness effectively captures sources of disease‐related atrophy that may differ across racial and ethnic groups. Cortical thickness had the strongest association with CDR. These results suggest that cortical thickness may be a more sensitive and generalizable marker of neurodegeneration than hippocampal volume or BAG in ethno‐racially diverse cohorts.


Long‐Term Post‐Stroke Cognition in Patients With Minor Ischemic Stroke is Related to Tract‐Based Disconnection Induced by White Matter Hyperintensities

January 2025

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18 Reads

Over a third of minor stroke patients experience post‐stroke cognitive impairment (PSCI), but no validated tools exist to identify at‐risk patients early. This study investigated whether disconnection features derived from infarcts and white matter hyperintensities (WMH) could serve as markers for short‐ and long‐term cognitive decline in first‐ever minor ischemic stroke patients. First‐ever minor ischemic stroke patients (NIHSS ≤ 7) were prospectively followed at 72‐h, 6 months, and 36 months post‐stroke with cognitive tests and brain MRI. Infarct and WMH volumes were semi‐automatically assessed on DWI and FLAIR sequences. Bayesian tract‐based disconnection models estimated remote pathological effects of infarcts and WMH. Associations between disconnection features and cognitive outcomes were analyzed using canonical correlation analyses, adjusted for age, education, and multiple comparisons. Among 105 patients (31% female, mean age 63 ± 12 years), infarct volume averaged 10.28 ± 17.10 cm³ and predominantly involved the middle cerebral artery territory (83%). WMH burden was higher in frontal periventricular white matter. Infarct‐based features did not significantly relate to PCSI. However, a WMH‐derived disconnection factor, involving commissural and frontal tracts, and the right superior longitudinal fasciculus, was significantly associated with PSCI at 6 months (OR = 9.96, p value = 0.02) and 36 months (OR = 12.27, p value = 0.006), particularly in executive/attention, language, and visuospatial domains. This factor, unrelated to WMH volume, outperformed demographic and clinical predictors of PSCI. WMH‐induced disconnection may be associated with short‐ and long‐term PSCI in minor stroke. Routine MR‐derived features could identify at‐risk patients for rehabilitation trials.


Flowchart of the containerized pipeline for pRF analyses. HeuDiConv converts DICOMs to NIfTI and curates them into BIDS format. fMRIPrep minimally preprocesses the data. prfprepare takes the preprocessed functional data in anatomical or surface space and prepares it for the pRF analysis: it selects only the voxels/vertices in V1‐3 to save computation time. prfanalyze performs pRF analysis using vistasoft. prfresult plots the results as coverage maps or surface overlays.
Coverage plots based on above‐threshold voxels/vertices. A. Coverage plots for surface (left) and volumetric (right) data for V1 in a representative subject. There are substantially more pRF centers (gray dots) in the surface results. The difference between the two coverages can be seen on the right. Already in this single subject, we can see a central coverage bias. B. For the group‐level comparison (N = 30), the coverage differences between surface and volume analyses show a higher foveal representation in the surface data. Effect sizes (Cohen's d) are shown for V1, V2, and V3, where red indicates greater coverage in the surface data. The observed systematic effect is only clearly visible across the whole group of subjects.
Group coverage surface‐volumetric differences in noiseless data and simulations to check for CNR and pRF size effects. Bootstrapped (50 repetitions with replacement) effect size (Cohen's d) representation of the surface‐volume difference in field of view coverage depending on the different analyses. The comparisons were paired. A. Noiseless data in volumetric versus its projection to the surface. Only voxels with more than 50% variance explained were included. B. Simulations to examine CNR effects. pRF size between the two conditions was kept constant, while the CNR differences were increased from left to right. With no CNR difference, the Cohen's d map equals a random field (leftmost panel). With CNR differences, a clear bias toward higher coverage in the fovea and lower coverage in the periphery appears (center and right panels). C. Simulations to examine pRF size effects. CNR was equated in the two conditions, but the pRF size for the surface condition was increased from left to right. With increasing difference a similar bias occurs, however, the bias in the foveal compared to peripheral areas was less pronounced.
Upsampling effects on the differences between surface and volumetric processing. A. Subsampled surface results compared to the full volumetric results, with an equal number of voxels/vertices. This analysis reproduces the central visual field bias. Thus, the number of data points is not the reason for the bias seen in the experimental results. B. Comparison of the surface data with a random subsample of the same data. No difference can be found in this analysis. C. Upsampled volumetric images (reslicing the voxels from 2 mm isotropic to 1 mm isotropic) compared to the original volumetric dataset. Here the same bias occurs as in panel A. To summarize, regardless of the number of data points, if there is upsampling (volumetric to surface or volumetric to volumetric), there is a foveal bias effect.
A. Linear cortical magnification function (CMF) for the early visual cortex areas V1 (blue), V2 (orange), and V3 (green) with their 95% confidence intervals, overlaid with previously published results (black) from (Horton and Hoyt 1991). The functions represent the square root of the estimated empirical areal CMF. B. CLF on our experimental data pRF results for V1 (blue), V2 (orange), and V3 (green). For comparison, the gray lines represent V1 results as reported by Strasburger (2022); Strasburger, Rentschler, and Juttner (2011) and based on different studies (a: (Larsson and Heeger 2006), b: (Duncan and Boynton 2003), c: (Cowey and Rolls 1974), d: (Schira et al. 2009), e: (Dougherty et al. 2003)). C. CLF of the primary visual cortex V1 with an exemplary regular sampling of the cortical distance (same distance on x‐axis). The colored bands indicate that the same amount of visual angle (gray) corresponds to different areas of the cortex (red). The sampling in the foveal areas is considerably denser than in more peripheral parts. When the data on the cortical level are linearly upsampled, the density difference between foveal and peripheral areas is intensified, leading to the effect observed in this study.
Biases in Volumetric Versus Surface Analyses in Population Receptive Field Mapping

January 2025

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17 Reads

Population receptive field (pRF) mapping is a quantitative functional MRI (fMRI) analysis method that links visual field positions with specific locations in the visual cortex. A common preprocessing step in pRF analyses involves projecting volumetric fMRI data onto the cortical surface, typically leading to upsampling of the data. This process may introduce biases in the resulting pRF parameters. Using publicly available analysis containers, we compared pRF maps generated from the original volumetric with those from upsampled surface data. Our results show substantial increases in pRF coverage in the central visual field of upsampled datasets. These effects were consistent across early visual cortex areas V1‐3. Further analysis indicates that this bias is primarily driven by the nonlinear relationship between cortical distance and visual field eccentricity, known as cortical magnification. Our results underscore the importance of understanding and addressing biases introduced by processing steps to ensure accurate interpretation of pRF mapping data, particularly in cross‐study comparisons.


fMRI Task. (A) Depiction of the Go and NoGo trials for the Go/NoGo task across the Aversive Emotion, Appetitive Drive, and Neutral Tasks. (B) Depiction of the Go and NoGo trials in the Cognitive Depletion task, which requires both inhibitory control (Go/NoGo) and working memory (N‐Back).
Location of each cluster that showed a significant Aversive versus Neutral × No‐Go versus Go interaction effect. Each color represents one of the 12 clusters reported in Table 2. R = Right hemisphere; L = Left hemisphere. L Agranular OFC/Agranular Insula/Frontal Operculum/IFG pars operculeris/IFG pars triangularis/MFG/dlPFC (red), L Inferior Lateral Occipital Cortex/ITG/MTG (blue), L Superior Lateral Occipital Cortex (pink), R Posterior Orbital Gyrus (Agranular OFC)/Insula (light blue), L Temporal Occipital Fusiform Gyrus (periwinkle), R ITG/Inferior Lateral Occipital Cortex (lime green), R Superior Lateral Occipital Cortex (dark green), L Parahippocampal Gyrus/Posterior Temporal Fusiform Cortex (orange), L Posterior Orbital Gyrus (Agranular OFC)/Insula (purple), L Paracingulate Gyrus/SFG (yellow), R Lateral Orbital Sulci (light pink), L Lateral Orbital Sulci (navy blue).
Location of each cluster that showed a significant Appetitive versus Neutral × No‐Go versus Go interaction effect. Each color represents one of the 10 clusters reported in Table 3. R = Right hemisphere; L = Left hemisphere. L Inferior/Superior Lateral Occipital Cortex/MTG (red), R Inferior/Superior Lateral Occipital Cortex/MTG (lime green), R Superior Parietal Lobule/Superior Lateral Occipital Cortex (blue), L IFG pars operculeris/MFG (pink), R Frontal Operculum/IFG, pars triangularis (light blue), R IFG pars operculeris/MFG/Precentral Gyrus (yellow), R Superior Parietal Lobule (orange), R Cuneus/Precuneus (purple), L OFC/Insula (peach), R Posterior MTG/STG (dark green).
Location of each cluster that showed a significant Cognitive versus Neutral × No‐Go versus Go interaction effect. Each color represents one of the six clusters reported in Table 4. R = Right hemisphere; L = Left hemisphere. Cross‐Cortex (blue), R Thalamus (red), B Posterior Cingulate (teal), L Caudate (lime green), R Middle Temporal Gyrus (yellow), L Putamen (orange).
Representative Visual Depiction of the Interaction Effect Patterns in Aversive, Appetitive, and Cognitive Depletion Tasks versus Neutral. Avr = Aversive, App = Appetitive, Cog = Cognitive, Neu = Neutral. Y‐axis reflects beta values derived from the GLM that quantifies the mean level activation in each task condition and trial type. Error bars reflect standard error across individuals for each cluster. (A) Left Lateral Orbital Frontal Sulci (cluster 12 in Table 2). A similar pattern of simple slopes was observed in seven clusters (clusters 1, 5–8, 10–11 in Table 2) for the Aversive Emotion contrast. (B) Left Agranular Orbital Frontal Cortex (OFC; cluster 4 in Table 2). A similar pattern of simple slopes was observed in three clusters (clusters 2, 3, and 9 in Table 2) for the Aversive Emotion contrast. (C) Left OFC/Insula (cluster 9 in Table 3). A similar pattern of simple slopes was observed in three clusters (clusters 5, 6, and 10 in Table 3) for the Appetitive Drive contrast. (D) Left IFG, pars opercularis (cluster 4 in Table 3). A similar pattern of simple slopes was observed in five clusters (clusters 1–3, 7, 8 in Table 3) for the Appetitive Drive contrast. (E) Cross‐Cortex (cluster 1 in Table 4). A similar pattern of simple slopes was observed in two clusters (clusters 3 and 5 in Table 4) for the Cognitive Depletion contrast. (F) Left caudate (cluster 4 in Table 4). A similar pattern of simple slopes was observed in two clusters (clusters 2 and 6 in Table 4) for the Cognitive Depletion contrast. *Significant cross‐valence, within condition effect (e.g., Avr vs. Neu during Go trials). Bolded lines indicate significant within‐valence, cross‐condition effects (e.g., NoGo vs. Go during Neutral). *p < 0.05, **p < 0.001.
Deciphering the Neural Effects of Emotional, Motivational, and Cognitive Challenges on Inhibitory Control Processes

January 2025

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18 Reads

Converging lines of research indicate that inhibitory control is likely to be compromised in contexts that place competing demands on emotional, motivational, and cognitive systems, potentially leading to damaging impulsive behavior. The objective of this study was to identify the neural impact of three challenging contexts that typically compromise self‐regulation and weaken impulse control. Participants included 66 healthy adults (M/SDage = 29.82/10.21 years old, 63.6% female) who were free of psychiatric disorders and psychotropic medication use. Participants completed a set of novel Go/NoGo (GNG) paradigms in the scanner, which manipulated contextual factors to induce (i) aversive emotions, (ii) appetitive drive, or (iii) concurrent working memory load. Voxelwise analysis of neural activation during each of these tasks was compared to that of a neutral GNG task. Findings revealed differential inhibition‐related activation in the aversive emotions and appetitive drive GNG tasks relative to the neutral task in frontal, parietal and temporal cortices, suggesting emotional and motivational contexts may suppress activation of these cortical regions during inhibitory control. In contrast, the GNG task with a concurrent working memory load showed widespread increased activation across the cortex compared to the neutral task, indicative of enhanced recruitment of executive control regions. Results suggest the neural circuitry recruited for inhibitory control varies depending on the concomitant emotional, motivational, and cognitive demands of a given context. This battery of GNG tasks can be used by researchers interested in studying unique patterns of neural activation associated with inhibitory control across three clinically relevant contexts that challenge self‐regulation and confer risk for impulsive behavior.


Measuring the effects of motion corruption in fetal fMRI

January 2025

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21 Reads

Irregular and unpredictable fetal movement is the most common cause of artifacts in in utero functional magnetic resonance imaging (fMRI), affecting analysis and limiting our understanding of early functional brain development. The accurate detection of corrupted functional connectivity (FC) resulting from motion artifacts or preprocessing, instead of neural activity, is a prerequisite for reliable and valid analysis of FC and early brain development. Approaches to address this problem in adult data are of limited utility in fetal fMRI. In this study, we evaluate a novel technique for robust computational assessment of motion artifacts, and the quantitative comparison of regression models for artifact removal in fetal FC analysis. It exploits the association between dynamic FC and non‐stationarity of fetal movement, to detect residual noise. To validate our motion artifact detection technique in detail, we used a parametric generative model for neural events and fMRI blood oxygenation level‐dependent (BOLD) signal. We conducted a systematic evaluation of 11 commonly used regression models in a sample of 70 fetuses with gestational age of 19–39 weeks. Results demonstrate that the proposed method has better accuracy in identifying corrupted FC compared to methods designed for adults. The technique, suggests that censoring, global signal regression and anatomical component‐based regression models are the most effective models for compensating motion. The benchmarking technique, and the generative model for realistic fetal fMRI BOLD enables investigators conducting in utero fMRI analysis to effectively quantify the impact of fetal motion and evaluate alternative regression strategies for mitigating this impact. The code is publicly available at: https://github.com/cirmuw/fetalfMRIproc.


Cholinergic‐specific Parkinson's disease‐related pattern, based on 34 PD patients and 10 healthy controls, after bootstrapping with 5000 repetitions, excluding voxels of which the 95% CI straddled zero. The color represents a positive (red/yellow) or negative (blue) pattern weight, which are interpreted as an increase or decrease in tracer uptake for patients compared to healthy controls, respectively.
Cholinergic‐specific cognition‐related pattern in Parkinson's disease (n = 34), after bootstrapping with 5000 repetitions, excluding voxels of which the 95% CI straddled zero. The color represents a positive (red/yellow) or negative (blue) pattern weight, which are interpreted as a positive or negative correlation between tracer uptake and cognition score, respectively. (A) Attention, (B) executive functioning, (C) visuospatial cognition, (D) memory.
Results of the disease‐related cross validation. Panel A shows the individual subject scores (dots) and boxplot for the healthy controls (left, n = 10) and Parkinson's disease patients (right, n = 34) (t = 6.85, p < 0.0001). The subject scores were obtained using a leave‐one‐out cross validation procedure, that is, by applying the obtained disease‐related brain covariance pattern, designed to distinguish patients from controls, to the left‐out subject. Panel B shows the receiver operating characteristic (ROC) curve corresponding to the data presented in panel A (PD vs. controls). The ROC curve had an area under the curve of 0.91.
Results of the cognition‐related leave‐one‐out cross validation. Panels (A–E) show the relation between subject scores for all cognitive domains (x‐axis) and the related cognitive performance (y‐axis) in Parkinson's disease patients (n = 34); that is, (A) attention (r = 0.36, p = 0.036), (B) executive functioning (r = 0.39, p = 0.022), (C) visuospatial cognition (r = 0.55, p = 0.001), (D) memory (r = 0.16, p = 0.364), (E) global cognition (r = 0.50, p = 0.003). Subject scores were obtained using a leave‐one‐out cross validation procedure, that is, by applying the obtained cognition‐related brain covariance pattern, designed to be associated to a specific domain, to the left‐out subject.
Cholinergic Denervation Patterns in Parkinson's Disease Associated With Cognitive Impairment Across Domains

January 2025

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22 Reads

Cognitive impairment is considered to be one of the key features of Parkinson's disease (PD), ultimately resulting in PD‐related dementia in approximately 80% of patients over the course of the disease. Several distinct cognitive syndromes of PD have been suggested, driven by different neurotransmitter deficiencies and thus requiring different treatment regimes. In this study, we aimed to identify characteristic brain covariance patterns that reveal how cholinergic denervation is related to PD and to cognitive impairment, focusing on four domains, including attention, executive functioning, memory, and visuospatial cognition. We applied scaled sub‐profile model principal component analysis to reveal cholinergic‐specific disease‐related and cognition‐related covariance patterns using [¹⁸F]fluoroethoxybenzovesamicol PET imaging. Stepwise logistic regression was applied to predict disease state (PD vs. healthy control). Linear regression models were applied to predict cognitive functioning within the PD group, for each cognitive domain separately. We assessed the performance of the identified patterns with leave‐one‐out cross validation and performed bootstrapping to assess pattern stability. We included 34 PD patients with various levels of cognitive dysfunction and 10 healthy controls, with similar age, sex, and educational level. The disease‐related cholinergic pattern was strongly discriminative (AUC 0.91), and was most prominent in posterior brain regions, with lower tracer uptake in patients compared to controls. We found largely overlapping cholinergic‐specific patterns across cognitive domains, with positive correlations between tracer uptake in the opercular cortex, left dorsolateral prefrontal cortex and posterior cingulate gyrus, among other regions, and attention, executive, and visuospatial functioning. Cross validation showed significant correlations between predicted and measured cognition scores, with the exception of memory. We identified a robust structural covariance pattern for the assessment of cholinergic dysfunction related to PD, as well as overlapping cholinergic patterns related to attentional, executive‐ and visuospatial impairment in PD patients.


Data preprocessing and R2* estimation workflow. (1) CNN‐based extraction of fetal brains from the original ME fMRI scans. Extracted fetal masks were motion‐corrected using MCFLIRT, and DVARS was estimated as a measure of head motion. (2) Ten consecutive fMRI volumes with the lowest DVARS for each echo were averaged to voxels within the averaged volume and were fitted to a monoexponential decay curve across the three echoes for T2* estimation. (3) T2* was used to calculate the weight for averaging the three echoes to obtain an optimally combined single fMRI image, which will be used for normalization estimation. (4) Motion‐corrected and masked fMRI images required manual reorientation to standard space. (5) Motion‐corrected fMRI data were normalized to the 32‐week GA template through a sequence of transformation matrices, using ANTs. (6) The transformation matrices estimated from fMRI images during steps 4 and 5 were then applied to the T2* maps to produce a T2* map normalized to the 32‐week GA template. (7) Normalized T2* maps were thresholded so that voxels with values outside the range of 50–400 ms were assigned a value of zero. The reciprocal of T2*, R2*, was then estimated and used for regional analyses.
Examples of T2* decay curve across 3 echo times (TEs). T2* decay curves for specific voxels (orange/red and light green/green) across echo 1 (18 ms), echo 2 (34 ms), and echo 3 (50 ms) represent the decay in T2* signal over time. Successful T2* curve fitting can be seen in the “Pass” example where the signal decays with time across the three echoes. Unsuccessful T2* curve fitting in specific voxels, as seen in the “Fail” example (orange and red), is due to an artifact present in the volume, resulting in higher signal intensity in a later echo compared to either earlier echo, leading to T2* curve‐fitting failure.
R2* increases with age across tissue segments. R2* significantly increases with GA in cortical gray matter in both the left and right hemispheres. R2* also increases significantly with GA in the left and right white matter but to a lesser degree as compared to in the gray matter. Lines connecting dots indicate longitudinal data from the same participant scanned at multiple time points. Shaded areas represent 95% confidence intervals.
Regional R2* increases with GA. R2* significantly increases with GA in regions within the motor cortex, cerebellum, temporal lobes, occipital lobes, hippocampus, putamen, and brain stem. Shaded areas represent 95% confidence intervals.
R2* levels significantly increase in the left insula with GA in females compared to males. R2* increases faster than males in the left insula with GA advancing. Lines indicate longitudinal data from the same participant scanned at multiple time points. Shaded gray areas represent 95% confidence intervals.
Whole Brain MRI Assessment of Age and Sex‐Related R2* Changes in the Human Fetal Brain

January 2025

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14 Reads

Iron in the brain is essential to neurodevelopmental processes, as it supports neural functions, including processes of oxygen delivery, electron transport, and enzymatic activity. However, the development of brain iron before birth is scarcely understood. By estimating R2* (1/T2*) relaxometry from a sizable sample of fetal multiecho echo‐planar imaging (EPI) scans, which is the standard sequence for functional magnetic resonance imaging (fMRI), across gestation, this study investigates age and sex‐related changes in iron, across regions and tissue segments. Our findings reveal that brain R2* levels significantly increase throughout gestation spanning many different regions, except the frontal lobe. Furthermore, females exhibit a faster rate of R2* increase compared to males, in both gray matter and white matter. This sex effect is particularly notable within the left insula. This work represents the first MRI examination of iron accumulation and sex differences in developing fetal brains. This is also the first study to establish R2* estimation methodology in fetal multiecho functional MRI.


Flow chart. Step 1: We used RSFC to characterize neural activity throughout the brain in stroke patients and healthy controls. Step 2: By comparing the global RSFC of HC and stroke patients, we identified abnormal patterns of RSFC. Step 3: Through PLS analysis, we identified significant covariance dimensions between symptoms and neuronal activity. Step 4: We employed cluster analysis to identify distinct subgroups (biotypes) within the patient population, thereby addressing the heterogeneity of PSA. Step 5: Using focal lesions as a natural causal model, we calculated the WM disconnection map for each patient and further computed the WM disconnection scores for each ROI. Step 6: We predicted the biotypes of the patients using a hypothesis‐driven model (RDN ROI) and a data‐driven model (whole‐brain ROI), respectively, and compared the predictive abilities of the two models. PLS, partial least squares; PSA, poststroke apathy; RDN, reward decision network; ROI, region of interest; RSFC, resting‐state functional connectivity; WM, white matter.
Defining biotypes using partial least squares (PLS) and clustering. (A) PLS component selection: We selected four PLS components based on their variance explained. The dashed line represents the average variance explained. The images in the top right corner depict the results of the permutation test. (B) Optimal cluster number: The Calinski–Harabasz index was computed for various cluster numbers using two brain scores. Cluster numbers = 4 demonstrated the best performance. Significance of clustering into four biotypes was confirmed based on the Calinski–Harabasz index across 2000 multivariate Gaussian distributions of brain scores. (C) Hierarchical clustering: Hierarchical clustering based on two brain scores identified four PSA biotypes. (D) Connectivity features: Neuroanatomical distribution of dysfunctional connectivity features that differed by biotype. Nodes are colored to indicate the biotype with the most abnormal connectivity features.
The relationship between reward decision network (RDN) damage and PSA heterogeneity. (A) Predictive performance comparison: The left panel compares the predictive performance of the RDN‐specific white matter disconnection model with those of the whole‐brain, VIS, and FPN white matter disconnection models. The right panel compares the predictive performance of the RDN‐specific white matter disconnection model with that of the null model. FPN, frontoparietal network; VIS, visual network. ***p < 0.001. (B) Anatomical distribution of important features: We extracted the top 10% of significant features from RDN‐specific model, primarily distributed across six clusters. The brightness of the colors corresponds to the level of importance, with brighter colors indicating higher levels of importance. (C) White matter disconnection scores across biotypes: White matter disconnection scores were computed for each biotype within different clusters. *represents the white matter disconnection score for this biotype that is significantly higher than the patient average (one‐tailed one‐sample t‐test, p < 0.05). ACC, anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; MFG, middle frontal gyrus; PCC, posterior cingulate cortex; SMA, supplementary motor area.
Reward Decision Network Disconnection in Poststroke Apathy: A Prospective Multimodality Imaging Study

January 2025

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16 Reads

Apathy is a common neuropsychiatric symptom following stroke, characterized by reduced goal‐directed behavior. The reward decision network (RDN), which plays a crucial role in regulating goal‐directed behaviors, is closely associated with apathy. However, the relationship between poststroke apathy (PSA) and RDN dysfunction remains unclear due to apathy heterogeneity, the confounding effect of depression and individual variability in lesion impacts. This study aims to dissect the heterogeneity of PSA and explore the link between lesion‐induced RDN damage and PSA. We prospectively recruited 207 patients with acute ischemic infarction and 60 demographically matched healthy controls. Participants underwent neuroimaging and longitudinal neuropsychiatric assessments. To characterize PSA heterogeneity, we employed multivariate analysis and clustering algorithms based on whole‐brain functional connectivity and clinical assessments to classify patients into different PSA biotypes. We embedded each patient's lesion into a structural connectome atlas to obtain white matter (WM) disconnection maps. On this basis, WM disconnection scores were calculated for each brain region to quantify lesion‐induced WM damage. We employed the XGBoost model to predict PSA biotypes based on WM disconnection scores, comparing the performance of models focusing on RDN‐specific versus whole‐brain WM disconnection. Additionally, we explored WM damage patterns across different biotypes by comparing disconnection scores in critical brain regions. We identified four PSA biotypes with unique clinical trajectories and neurobiological underpinnings. Biotype 4 was characterized by persistent apathy with depressive symptoms. Biotype 2 showed persistent apathy. Biotype 3 was non‐apathetic. Biotype 1 exhibited delayed‐onset apathy. The XGBoost models, when focused on the RDN‐specific WM disconnection, performed significantly better in predicting PSA biotypes compared to the whole‐brain WM disconnection model (t(164.66) = 8.871, p < 0.001). Analysis of WM disconnection patterns revealed that Biotype 4 exhibited more extensive RDN damage in crucial regions, Biotype 1 had a unique pattern of damage in the anterior cingulate cortex (t(61) = 1.874, p = 0.032), and Biotype 2 had a unique pattern of damage in the orbitofrontal cortex (t(53)= 1.827, p = 0.036). This study dissected PSA heterogeneity and demonstrated that RDN damage is a critical factor in PSA variability. We found that lesion‐induced WM disconnections in anterior cingulate cortex and orbitofrontal cortex can lead to delayed‐onset and persistent apathy, respectively. Furthermore, our findings revealed that apathy not only has distinct pathogenic mechanisms, but also shares neurobiological substrates with depression.


Journal metrics


3.5 (2023)

Journal Impact Factor™


27%

Acceptance rate


8.3 (2023)

CiteScore™


16 days

Submission to first decision


$3,200 / £2,610 / €2,930

Article processing charge

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