A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Characterizing the microstructural transition at the gray matter-white matter interface: Implementation and demonstration of age-associated differences

BACKGROUND

The cortical gray matter-white matter interface (GWI) is a natural transition zone where the composition of brain tissue abruptly changes and is a location for pathologic change in brain disorders. While diffusion magnetic resonance imaging (dMRI) is a reliable and well-established technique to characterize brain microstructure, the GWI is difficult to assess with dMRI due to partial volume effects and is normally excluded from such studies.

METHODS

In this study, we introduce an approach to characterize the dMRI microstructural profile across the GWI and to assess the sharpness of the microstructural transition from cortical gray matter (GM) to white matter (WM). This analysis includes cross-sectional data from a total of 146 participants (18-91 years; mean age: 52.4 (SD 21.4); 83 (57 %) female) enrolled in two normative lifespan cohorts at Albert Einstein College of Medicine from 2019 to 2023. We compute the aggregate GWI slope for each parameter, across each of 6 brain regions (cingulate, frontal, occipital, orbitofrontal, parietal, and temporal) for each participant. The association of GWI slope in each region with age was assessed using a linear model, with biological sex as a covariate.

RESULTS

We demonstrate this method captures an inherent change in fractional anisotropy (FA), axial diffusivity (AD), orientation dispersion index (ODI) and intracellular volume fraction (ICVF) across the GWI that is characterized by small variance. We identified statistically significant associations of FA slope with age in all regions (p < 0.002 for all analyses), with FA slope magnitude inversely associated with higher age. Similar statistically significant age-related associations were found for AD slope in cingulate, occipital, and temporal regions, for ODI slope in parietal and occipital regions, and for ICVF slope in frontal, orbitofrontal, parietal, and temporal regions.

CONCLUSION

The inverse association of slope magnitude with age indicates loss of the sharp GWI transition in aging, which is consistent with processes such as dendritic pruning, axonal degeneration, and inflammation. This method overcomes techniques issues related to interrogating the GWI. Beyond characterizing normal aging, it could be applied to explore pathological effects at this crucial, yet under-researched region.

Effects of acupuncture at the Taichong (LIV3) and Hegu (LI4) points on functional connectivity with the retrosplenial cortex in patients with Alzheimer's disease

Background

Acupuncture has been demonstrated to have a promising effect on Alzheimer's disease (AD), but the underlying neural mechanisms remain unclear. The retrosplenial cortex (RSC) is one of the earliest brain regions affected in AD, and changes in its functional connectivity (FC) are reported to underlie disease-associated memory impairment. The aim of this study was to examine the effect of acupuncture on FC with the RSC in patients with AD.

Methods

Demographic data, neuropsychological assessments, and resting-state functional magnetic resonance imaging (fMRI) data were collected from 14 AD patients and 14 normal controls (NCs) matched by age, sex, and educational level at baseline. After the baseline MRI scan, acupuncture stimulation on the Taichong (LIV3) and Hegu (LI4) points was performed for 3 min. Then, another 10 min of fMRI data were acquired after the needle was withdrawn. A dataset that included 100 healthy participants was also included to construct a reliable FC map of the RSC. Two sets of regions of interest (ROIs) in the RSC were selected to assess the sustained effect of acupuncture on FC with the RSC in AD patients and NCs.

Results

Two sets of RSC ROI-based analyses demonstrated robust positive connectivity with the hippocampus (HPC). Furthermore, multiple brain regions, including the bilateral thalamus, bilateral posterior cingulate cortex (PCC), bilateral subcallosal cingulate gyrus (SCG), bilateral orbitofrontal cortex (OFC), and right precuneus, showed decreased FC with the RSC in the AD group and increased FC with the RSC in the NC group after acupuncture compared to that at baseline. Acupuncture also specifically elicited increased FC between the RSC and the HPC as well as between the RSC and the parahippocampal gyrus in AD patients and decreased FC between the RSC and the visual cortices in NCs. Additionally, diminished FC with the RSC was correlated with neuropsychological scale scores in the AD group before acupuncture treatment.

Conclusion

These findings confirm and extend previous studies suggesting that acupuncture at Taichong (LIV3) and Hegu (LI4) can exert bidirectional and benign regulatory effects on RSC connectivity in AD patients.

Aberrant functional connectivity between the retrosplenial cortex and hippocampal subregions in amnestic mild cognitive impairment and Alzheimer's disease

The posterior cingulate cortex and hippocampus are the core regions involved in episodic memory, and they exhibit functional connectivity changes in the development and progression of Alzheimer's disease. Previous studies have demonstrated that the posterior cingulate cortex and hippocampus are both cytoarchitectonically heterogeneous regions. Specifically, the retrosplenial cortex, typically subsumed under the posterior cingulate cortex, is an area functionally and anatomically distinct from the posterior cingulate cortex, and the hippocampus is composed of several subregions that participate in multiple cognitive processes. However, little is known about the functional connectivity patterns of the retrosplenial cortex or other parts of the posterior cingulate cortex with hippocampal subregions and their differential vulnerability to Alzheimer's disease pathology. Demographic data, neuropsychological assessments, and resting-state functional magnetic resonance imaging data were collected from 60 Alzheimer's disease participants, 60 participants with amnestic mild cognitive impairment, and 60 sex-matched normal controls. The bilateral retrosplenial cortex, other parts of the posterior cingulate cortex, and hippocampus subregions (including the bilateral anterior hippocampus and posterior hippocampus) were selected to investigate functional connectivity alterations in amnestic mild cognitive impairment and Alzheimer's disease. Resting-state functional connectivity analysis demonstrated heterogeneity in the degree of connectivity between the hippocampus and different parts of the total posterior cingulate cortex, with considerably greater functional connectivity of the retrosplenial cortex with the hippocampus compared with other parts of the posterior cingulate cortex. Furthermore, the bilateral retrosplenial cortex exhibited widespread intrinsic functional connectivity with all anterior-posterior hippocampus subregions. Compared to the normal controls, the amnestic mild cognitive impairment and Alzheimer's disease groups showed different magnitudes of decreased functional connectivity between the retrosplenial cortex and the contralateral posterior hippocampus. Additionally, diminished functional connectivity between the left retrosplenial cortex and right posterior hippocampus was correlated with clinical disease severity in amnestic mild cognitive impairment subjects, and the combination of multiple functional connectivity indicators of the retrosplenial cortex can discriminate the three groups from each other. These findings confirm and extend previous studies suggesting that the retrosplenial cortex is extensively and functionally connected with hippocampus subregions and that these functional connections are selectively affected in the Alzheimer's disease continuum, with prominent disruptions in functional connectivity between the retrosplenial cortex and contralateral posterior hippocampus underpinning episodic memory impairment associated with the disease.

Eeg based smart emotion recognition using meta heuristic optimization and hybrid deep learning techniques

In the domain of passive brain-computer interface applications, the identification of emotions is both essential and formidable. Significant research has recently been undertaken on emotion identification with electroencephalogram (EEG) data. The aim of this project is to develop a system that can analyse an individual's EEG and differentiate among positive, neutral, and negative emotional states. The suggested methodology use Independent Component Analysis (ICA) to remove artefacts from Electromyogram (EMG) and Electrooculogram (EOG) in EEG channel recordings. Filtering techniques are employed to improve the quality of EEG data by segmenting it into alpha, beta, gamma, and theta frequency bands. Feature extraction is performed with a hybrid meta-heuristic optimisation technique, such as ABC-GWO. The Hybrid Artificial Bee Colony and Grey Wolf Optimiser are employed to extract optimised features from the selected dataset. Finally, comprehensive evaluations are conducted utilising DEAP and SEED, two publically accessible datasets. The CNN model attains an accuracy of approximately 97% on the SEED dataset and 98% on the DEAP dataset. The hybrid CNN-ABC-GWO model achieves an accuracy of approximately 99% on both datasets, with ABC-GWO employed for hyperparameter tuning and classification. The proposed model demonstrates an accuracy of around 99% on the SEED dataset and 100% on the DEAP dataset. The experimental findings are contrasted utilising a singular technique, a widely employed hybrid learning method, or the cutting-edge method; the proposed method enhances recognition performance.

Correlation Between Swallowing and Cognitive Brain Networks in Older Adults via Functional MRI

BACKGROUND

Clinical evidence supports that swallowing function is correlated with cognition, but the neurobiological mechanism associated with cognitive impairment and dysphagia remains unclear.

OBJECTIVES

To compare the brain activation patterns of the swallowing and the cognitive tasks and explore neural associations between swallowing and cognitive function via task-related functional magnetic resonance imaging (fMRI).

METHODS

A total of 13 healthy older adults (aged > 60 years) were recruited. Participants underwent the clinical dementia rating (CDR) test and the Montreal Cognitive Assessment (MoCA). A block-designed task-related fMRI study was conducted where each participant completed both swallowing and cognitive tasks within a single session. During the swallowing task, participants swallowed 2 mL of thickened water, while the Stroop Colour Word Test (SCWT) served as the cognitive task. First-level analysis of swallowing time-series images utilised the general linear model (GLM) with Statistical Parametric Mapping (SPM), applying a voxel threshold of p < 0.001 for significance. Common activations in brain regions during swallowing and cognitive tasks were extracted at the group level, with significance set at p < 0.05, corrected for multiple comparisons using the false discovery rate (FDR), with a minimum cluster size of 20 voxels. Correlation analysis between behavioural measurements and imaging signals was also conducted.

RESULTS

Some regions were commonly activated in both task networks; these regions were the bilateral occipital lobe, cerebellum, lingual gyrus, fusiform, middle frontal gyrus, precentral and postcentral gyrus, right supramarginal and inferior parietal lobe. Most importantly, the average beta value of cognitive and swallowing tasks in these areas are both significantly negative related to the MoCA score. Furthermore, opposite signal changes were seen in the bilateral prefrontal lobes during the swallowing task, while positive activation in the bilateral prefrontal lobes was observed during the SCWT. Postcentral gyrus activation was more extensive than precentral gyrus activation in the swallowing task.

CONCLUSION

The common activation of swallowing and cognitive tasks had multiple foci. The activity of cognitive and swallowing task in these areas is significantly negative correlated with the MoCA score. These findings may help to illustrate the association between dysphagia and cognitive impairment due to the common brain regions involved in cognition and swallowing and may provide a reference for further rehabilitation of dysphagia.

TRIAL REGISTRATION

Clinical Trial: (Chinese Clinical Trial Registry): ChiCTR1900021795.

Quantifying neurovascular coupling through a concurrent assessment of arterial, capillary, and neuronal activation in humans: A multimodal EEG-fNIRS-TCD investigation

BACKGROUND

This study explored a novel multimodal neuroimaging approach to assess neurovascular coupling (NVC) in humans using electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and transcranial Doppler ultrasound (TCD).

METHODS

Fifteen participants (nine females; age 19-32) completed concurrent EEG-fNIRS-TCD imaging during motor (finger tapping) and visual ("Where's Waldo?") tasks, with synchronized monitoring of blood pressure, capnography, and heart rate. fNIRS assessed microvascular oxygenation within the frontal, motor, parietal, and occipital cortices, while the middle and posterior cerebral arteries (MCA/PCA) were insonated using TCD. A 16-channel EEG set-up was placed according to the 10-20 system. Wilcoxon signed-rank tests were used to compare physiological responses between the active and resting phases of the tasks, while cross-correlations with zero legs compared cerebral and systemic hemodynamic responses across both tasks.

RESULTS

Time-frequency analysis demonstrated a reduction in alpha and low beta band power in electrodes C3/C4 during finger tapping (p<0.045) and all electrodes during the Waldo task (all p<0.001). During Waldo, cross-correlation analysis demonstrated the change in oxygenated hemoglobin and cerebral blood velocity had a moderate-to-strong negative correlation with systemic physiological influences, highlighting the measured change resulted from neuronal input. Deoxygenated hemoglobin displayed the greatest negative cross-correlation with the MCA/PCA within the motor cortices and visual during the motor and visual tasks, respectively (range:0.54, -0.82).

CONCLUSIONS

This investigation demonstrated the feasibility of the proposed EEG-fNIRS-TCD response to comprehensively assess the NVC response within human, specifically quantifying the real-time temporal synchrony between neuronal activation (EEG), microvascular oxygenation changes (fNIRS), and conduit artery velocity alterations (TCD).

The abnormally increased functional connectivity of the locus coeruleus in migraine without aura patients

BACKGROUND

The neurovascular theory is thought to be one of the main pathological mechanisms of migraine. Locus coeruleus (LC) is a major node in the neurovascular pathway. Exploring the functional network characteristics of LC in migraine without aura (MwoA) patients can help us gain insight into the underlying neural mechanisms in MwoA patients.

METHODS

In this study, we used resting-state functional magnetic resonance imaging (rsfMRI) and a functional connectivity (FC) approach to explore the functional characteristics of LC in MwoA patients. 17 healthy controls (HCs) and 28 MwoA patients were included in the study. FC was calculated based on rsfMRI data collected by a 3T MRI scanner. General linear model were used to compare whether there were differences in LC brain networks between the two groups. We also utilized logistic regression to explore the role of LC functional networks in the clinical diagnosis of MwoA.

RESULTS

After general linear analysis, MwoA patients displayed increased FC from right LC to the left lingual and calcarine sulcus, as well as to the right frontal medial gyrus/orbit part, when compared with HCs. The results of the logistic regression showed that the LC FC signals were 81% accurate in distinguishing MwoA from the HCs.

CONCLUSION

Our results demonstrated that patients with MwoA exhibited significant LC FC differences in the brain areas associated with visual and cognitive function. Understanding the changes in the LC brain network in MwoA patients can provide us with new ideas to understand the pathological mechanisms of MwoA.

Functional connectivity of the pediatric brain

PURPOSE

This review highlights the importance of functional connectivity in pediatric neuroscience, focusing on its role in understanding neurodevelopment and potential applications in clinical practice. It discusses various techniques for analyzing brain connectivity and their implications for clinical interventions in neurodevelopmental disorders.

METHODS

The principles and applications of independent component analysis and seed-based connectivity analysis in pediatric brain studies are outlined. Additionally, the use of graph analysis to enhance understanding of network organization and topology is reviewed, providing a comprehensive overview of connectivity methods across developmental stages, from fetuses to adolescents.

RESULTS

Findings from the reviewed studies reveal that functional connectivity research has uncovered significant insights into the early formation of brain circuits in fetuses and neonates, particularly the prenatal origins of cognitive and sensory systems. Longitudinal research across childhood and adolescence demonstrates dynamic changes in brain connectivity, identifying critical periods of development and maturation that are essential for understanding neurodevelopmental trajectories and disorders.

CONCLUSION

Functional connectivity methods are crucial for advancing pediatric neuroscience. Techniques such as independent component analysis, seed-based connectivity analysis, and graph analysis offer valuable perspectives on brain development, creating new opportunities for early diagnosis and targeted interventions in neurodevelopmental disorders, thereby paving the way for personalized therapeutic strategies.

Resting state of human brain measured by fMRI experiment is governed more dominantly by essential mode as a global signal rather than default mode network

Resting-state of the human brain has been described by a combination of various basis modes including the default mode network (DMN) identified by fMRI BOLD signals in human brains. Whether DMN is the most dominant representation of the resting-state has been under question. Here, we investigated the unexplored yet fundamental nature of the resting-state. In the absence of global signal regression for the analysis of brain-wide spatial activity pattern, the fMRI BOLD spatiotemporal signals during the rest were completely decomposed into time-invariant spatial-expression basis modes (SEBMs) and their time-evolution basis modes (TEBMs). Contrary to our conventional concept above, similarity clustering analysis of the SEBMs from 166 human brains revealed that the most dominant SEBM cluster is an asymmetric mode where the distribution of the sign of the components is skewed in one direction, for which we call essential mode (EM), whereas the second dominant SEBM cluster resembles the spatial pattern of DMN. Having removed the strong 1/f noise in the power spectrum of TEBMs, the genuine oscillatory behavior embedded in TEBMs of EM and DMN-like mode was uncovered around the low-frequency range below 0.2 Hz.

Temporal complexity of the BOLD-signal in preterm versus term infants

Preterm birth causes alterations in structural and functional cerebral development that are not fully understood. Here, we investigate whether basic characteristics of BOLD signal itself might differ across preterm, term equivalent, and term infants. Anatomical, fMRI, and diffusion weighted imaging data from 716 neonates born at 23-43 weeks gestational age were obtained from the Developing Human Connectome Project. Hurst exponent (H; a measure of temporal complexity of a time-series) was computed from the power spectral density of the BOLD signal within 13 resting state networks. Using linear mixed effects models to account for scan age and birth age, we found that H increased with age, that earlier birth age contributed to lower H values, and that H increased most in motor and sensory networks. We then tested for a relationship between temporal complexity and structural development using H and DTI-based estimates of myelination and found moderate but significant correlations. These findings suggest that the temporal complexity of BOLD signal in neonates relates to age and tracks with known developmental trajectories in the brain. Elucidating how these signal-based differences might relate to maturing hemodynamics in the preterm brain could yield new information about neurophysiological vulnerabilities during this crucial developmental period.

Emotional contagion and cognitive empathy regulate the effect of depressive symptoms on empathy-related brain functional connectivity in patients with chronic back pain

BACKGROUND

Chronic pain and depression share common neural mechanisms, but their impacts on empathy are different. It is unclear how comorbid depressive symptoms affect empathy-related brain function in patients with chronic pain.

METHODS

A total of 29 healthy participants and 107 patients with chronic back pain (CBP) were included in this study. All subjects underwent a functional MRI scan with concurrent empathic stimulation. Multiple linear regression, moderation analysis, and mediation analysis were used to explore the impacts of chronic pain and comorbid depression on empathy.

RESULTS

The interaction between the pain intensity and the depressive symptoms affected the functional connectivity (FC) of the insula-middle frontal gyrus (MFG), and the severity of the self-rating depression scale (SDS) scores moderated the effect of the pain on the left insula-left MFG FC. Within the CBP group, the emotional contagion (EC) scores served as a mediator in the association between the SDS scores and the FC of the left middle cingulate cortex (MCC)-inferior temporal gyrus (ITG), and the level of cognitive empathy (CE) moderated the effect of the SDS scores on the left MCC-ITG FC.

LIMITATIONS

There is a lack of research on the effects of depressive symptoms on empathy in individuals with different types of chronic pain.

CONCLUSION

Depressive symptoms were strongly associated with the emotional contagion in patients with chronic back pain. Furthermore, the emotional contagion and the cognitive empathy regulated the effect of the comorbid depressive symptoms on the MCC-ITG connectivity in patients with chronic back pain.

Functional connectivity development in the prenatal and neonatal stages measured by functional magnetic resonance imaging: A systematic review

The prenatal and neonatal periods are two of the most important developmental stages of the human brain. It is therefore crucial to understand normal brain development and how early connections are established during these periods, in order to advance the state of knowledge on altered brain development and eventually identify early brain markers of neurodevelopmental disorders and diseases. In this systematic review (Prospero ID: CRD42024511365), we compiled resting state functional magnetic resonance imaging (fMRI) studies in healthy fetuses and neonates, in order to outline the main characteristics of typical development of the functional brain connectivity during the prenatal and neonatal periods. A systematic search of five databases identified a total of 12 573 articles. Of those, 28 articles met pre-established selection criteria based determined by the authors after surveying and compiling the major limitations reported within the literature. Inclusion criteria were: (1) resting state studies; (2) presentation of original results; (3) use of fMRI with minimum one Tesla; (4) a population ranging from 20 weeks of GA to term birth (around 37-42 weeks of PMA); (5) singleton pregnancy with normal development (absence of any complications known to alter brain development). Exclusion criteria were: (1) preterm studies; (2) post-mortem studies; (3) clinical or pathological studies; (4) twin studies; (5) papers with a sole focus on methodology (i.e. focused on tool and analysis development); (6) volumetric studies; (7) activation map studies; (8) cortical analysis studies; (9) conference papers. A risk of bias assessment was also done to evaluate each article's methodological rigor. 1877 participants were included across all the reviewed articles. Results consistently revealed a developmental gradient of increasing functional brain connectivity from posterior to anterior regions and from proximal-to-distal regions. A decrease in local small-world organization shortly after birth was also observed; small-world characteristics were present in fetuses and newborns, but appeared weaker in the latter group. Also, the posterior-to-anterior gradient could be associated with earlier development of the sensorimotor networks in the posterior regions while more complex higher-order networks (e.g. attention-related) mature later in the anterior regions. The main limitations of this systematic review stem from the inherent limitations of functional imaging in fetuses, mainly: unevenly distributed populations and limited sample sizes; fetal movements in the womb and other imaging obstacles; and a large voxel resolution when imaging a small brain. Another limitation specific to this review is the relatively small number of included articles compared to very a large search result, which may have led to relevant articles having been overlooked.

ASD-GANNet: A Generative Adversarial Network-Inspired Deep Learning Approach for the Classification of Autism Brain Disorder

The classification of a pre-processed fMRI dataset using functional connectivity (FC)-based features is considered a challenging task because of the set of high-dimensional FC features and the small dataset size. To tackle this specific set of FC high-dimensional features and a small-sized dataset, we propose here a conditional Generative Adversarial Network (cGAN)-based dataset augmenter to first train the cGAN on computed connectivity features of NYU dataset and use the trained cGAN to generate synthetic connectivity features per category. After obtaining a sufficient number of connectivity features per category, a Multi-Head attention mechanism is used as a head for the classification. We name our proposed approach "ASD-GANNet", which is end-to-end and does not require hand-crafted features, as the Multi-Head attention mechanism focuses on the features that are more relevant. Moreover, we compare our results with the six available state-of-the-art techniques from the literature. Our proposed approach results using the "NYU" site as a training set for generating a cGAN-based synthetic dataset are promising. We achieve an overall 10-fold cross-validation-based accuracy of 82%, sensitivity of 82%, and specificity of 81%, outperforming available state-of-the art approaches. A sitewise comparison of our proposed approach also outperforms the available state-of-the-art, as out of the 17 sites, our proposed approach has better results in the 10 sites.

Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task

Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples (Ns: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the Neutral cue. This effect was largest for the Cue Model parameterization; however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.

Neural underpinnings of individual differences in emotion regulation: A systematic review

This review synthesises individual differences in neural processes related to emotion regulation (ER). It comprises individual differences in self-reported and physiological regulation success, self-reported ER-related traits, and demographic variables, to assess their correlation with brain activation during ER tasks. Considering region-of-interest (ROI) and whole-brain analyses, the review incorporated data from 52 functional magnetic resonance imaging studies. Results can be summarized as follows: (1) Self-reported regulation success (assessed by emotional state ratings after regulation) and self-reported ER-related traits (assessed by questionnaires) correlated with brain activity in the lateral prefrontal cortex. (2) Amygdala activation correlated with ER-related traits only in ROI analyses, while it was associated with regulation success in whole-brain analyses. (3) For demographic and physiological measures, there was no systematic overlap in effects reported across studies. In showing that individual differences in regulation success and ER-related traits can be traced back to differences in the neural activity of brain regions associated with emotional reactivity (amygdala) and cognitive control (lateral prefrontal cortex), our findings can inform prospective personalised intervention models.

MR Imaging in Ataxias: Consensus Recommendations by the Ataxia Global Initiative Working Group on MRI Biomarkers

With many viable strategies in the therapeutic pipeline, upcoming clinical trials in hereditary and sporadic degenerative ataxias will benefit from non-invasive MRI biomarkers for patient stratification and the evaluation of therapies. The MRI Biomarkers Working Group of the Ataxia Global Initiative therefore devised guidelines to facilitate harmonized MRI data acquisition in clinical research and trials in ataxias. Recommendations are provided for a basic structural MRI protocol that can be used for clinical care and for an advanced multi-modal MRI protocol relevant for research and trial settings. The advanced protocol consists of modalities with demonstrated utility for tracking brain changes in degenerative ataxias and includes structural MRI, magnetic resonance spectroscopy, diffusion MRI, quantitative susceptibility mapping, and resting-state functional MRI. Acceptable ranges of acquisition parameters are provided to accommodate diverse scanner hardware in research and clinical contexts while maintaining a minimum standard of data quality. Important technical considerations in setting up an advanced multi-modal protocol are outlined, including the order of pulse sequences, and example software packages commonly used for data analysis are provided. Outcome measures most relevant for ataxias are highlighted with use cases from recent ataxia literature. Finally, to facilitate access to the recommendations by the ataxia clinical and research community, examples of datasets collected with the recommended parameters are provided and platform-specific protocols are shared via the Open Science Framework.

Simultaneous fMRI and metabolic MRS of hyperpolarized [1-13C]pyruvate during nicotine stimulus in rat

Functional MRI (fMRI) and MRS (fMRS) can be used to noninvasively map cerebral activation and metabolism. Recently, hyperpolarized 13C spectroscopy and metabolic imaging have provided an alternative approach to assess metabolism. In this study, we combined 1H fMRI and hyperpolarized [1-13C]pyruvate MRS to compare cerebral blood oxygenation level-dependent (BOLD) response and real-time cerebral metabolism, as assessed with lactate and bicarbonate labelling, during nicotine stimulation. Simultaneous 1H fMRI (multislice gradient echo echo-planar imaging) and 13C spectroscopic (single slice pulse-acquire) data were collected in urethane-anaesthetized female Sprague-Dawley rats (n = 12) at 9.4 T. Animals received an intravenous (i.v.) injection of either nicotine (stimulus; 88 μg/kg, n = 7, or 300 μg/kg, n = 5) or 0.9% saline (matching volume), followed by hyperpolarized [1-13C]pyruvate injection 60 s later. Three hours later, a second injection was administered: the animals that had previously received saline were injected with nicotine and vice versa, both followed by another hyperpolarized [1-13C]pyruvate i.v. injection 60 s later. The low-dose (88 μg/kg) nicotine injection led to a 12% ± 4% (n = 7, t-test, p ~ 0.0006 (t-value -5.8, degrees of freedom 6), Wilcoxon p ~ 0.0078 (test statistic 0)) increase in BOLD signal. At the same time, an increase in 13C-bicarbonate signal was seen in four out of six animals. Bicarbonate-to-total carbon ratios were 0.010 ± 0.004 and 0.018 ± 0.010 (n = 6, t-test, p ~ 0.03 (t-value -2.3, degrees of freedom 5), Wilcoxon p ~ 0.08 (test statistic 3)) for saline and nicotine experiments, respectively. No increase in the lactate signal was seen; lactate-to-total carbon was 0.16 ± 0.02 after both injections. The high (300 μg/kg) nicotine dose (n = 5) caused highly variable BOLD and metabolic responses, possibly due to the apparent respiratory distress. Simultaneous detection of 1H fMRI and hyperpolarized 13C-MRS is feasible. A comparison of metabolic response between control and stimulated states showed differences in bicarbonate signal, implying that the hyperpolarization technique could offer complimentary information on brain activation.

Brain connectivity changes underlying depression and fatigue in relapsing-remitting multiple sclerosis: A systematic review

Multiple Sclerosis (MS) is an autoimmune disease affecting the central nervous system, characterised by neuroinflammation and neurodegeneration. Fatigue and depression are common, debilitating, and intertwined symptoms in people with relapsing-remitting MS (pwRRMS). An increased understanding of brain changes and mechanisms underlying fatigue and depression in RRMS could lead to more effective interventions and enhancement of quality of life. To elucidate the relationship between depression and fatigue and brain connectivity in pwRRMS we conducted a systematic review. Searched databases were PubMed, Web-of-Science and Scopus. Inclusion criteria were: studied participants with RRMS (n ≥ 20; ≥ 18 years old) and differentiated between MS subtypes; published between 2001-01-01 and 2023-01-18; used fatigue and depression assessments validated for MS; included brain structural, functional magnetic resonance imaging (fMRI) or diffusion MRI (dMRI). Sixty studies met the criteria: 18 dMRI (15 fatigue, 5 depression) and 22 fMRI (20 fatigue, 5 depression) studies. The literature was heterogeneous; half of studies reported no correlation between brain connectivity measures and fatigue or depression. Positive findings showed that abnormal cortico-limbic structural and functional connectivity was associated with depression. Fatigue was linked to connectivity measures in cortico-thalamic-basal-ganglial networks. Additionally, both depression and fatigue were related to altered cingulum structural connectivity, and functional connectivity involving thalamus, cerebellum, frontal lobe, ventral tegmental area, striatum, default mode and attention networks, and supramarginal, precentral, and postcentral gyri. Qualitative analysis suggests structural and functional connectivity changes, possibly due to axonal and/or myelin loss, in the cortico-thalamic-basal-ganglial and cortico-limbic network may underlie fatigue and depression in pwRRMS, respectively, but the overall results were inconclusive, possibly explained by heterogeneity and limited number of studies. This highlights the need for further studies including advanced MRI to detect more subtle brain changes in association with depression and fatigue. Future studies using optimised imaging protocols and validated depression and fatigue measures are required to clarify the substrates underlying these symptoms in pwRRMS.

An advance in novel intelligent sensory technologies: From an implicit-tracking perspective of food perception

Food sensory evaluation mainly includes explicit and implicit measurement methods. Implicit measures of consumer perception are gaining significant attention in food sensory and consumer science as they provide effective, subconscious, objective analysis. A wide range of advanced technologies are now available for analyzing physiological and psychological responses, including facial analysis technology, neuroimaging technology, autonomic nervous system technology, and behavioral pattern measurement. However, researchers in the food field often lack systematic knowledge of these multidisciplinary technologies and struggle with interpreting their results. In order to bridge this gap, this review systematically describes the principles and highlights the applications in food sensory and consumer science of facial analysis technologies such as eye tracking, facial electromyography, and automatic facial expression analysis, as well as neuroimaging technologies like electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and functional near-infrared spectroscopy. Furthermore, we critically compare and discuss these advanced implicit techniques in the context of food sensory research and then accordingly propose prospects. Ultimately, we conclude that implicit measures should be complemented by traditional explicit measures to capture responses beyond preference. Facial analysis technologies offer a more objective reflection of sensory perception and attitudes toward food, whereas neuroimaging techniques provide valuable insight into the implicit physiological responses during food consumption. To enhance the interpretability and generalizability of implicit measurement results, further sensory studies are needed. Looking ahead, the combination of different methodological techniques in real-life situations holds promise for consumer sensory science in the field of food research.

Brain fiber structure estimation based on principal component analysis and RINLM filter

Diffusion magnetic resonance imaging is a technique for non-invasive detection of microstructure in the white matter of the human brain, which is widely used in neuroscience research of the brain. However, diffusion-weighted images(DWI) are sensitive to noise, which affects the subsequent reconstruction of fiber orientation direction, microstructural parameter estimation and fiber tracking. In order to better eliminate the noise in diffusion-weighted images, this study proposes a noise reduction method combining Marchenko-Pastur principal component analysis(MPPCA) and rotation-invariant non-local means filter(RINLM) to further remove residual noise and preserve the image texture detail information. In this study, the algorithm is applied to the fiber structure and the prevailing microstructural models within the human brain voxels based on simulated and real human brain datasets. Experimental comparisons between the proposed method and the state-of-the-art methods are performed in single-fiber, multi-fiber, crossed and curved-fiber regions as well as in different microstructure estimation models. Results demonstrated the superior performance of the proposed method in denoising DWI data, which can reduce the angular error in fiber orientation reconstruction to obtain more valid fiber structure estimation and enable more complete fiber tracking trajectories with higher coverage. Meanwhile, the method reduces the estimation errors of various white matter microstructural parameters and verifies the performance of the method in white matter microstructure estimation.

Reduced default mode network effective connectivity in healthy aging is modulated by years of education

Aging is a major risk factor for neurodegenerative diseases like dementia and Alzheimer's disease. Even in non-pathological aging, decline in cognitive functioning is observed in the majority of the elderly population, necessitating the importance of studying the processes involved in healthy aging in order to identify brain biomarkers that promote the conservation of functioning. The default mode network (DMN) has been of special interest to aging research due to its vulnerability to atrophy and functional decline over the course of aging. Prior work has focused almost exclusively on functional (i.e. undirected) connectivity, yet converging findings are scarce. Therefore, we set out to use spectral dynamic causal modeling to investigate changes in the effective (i.e. directed) connectivity within the DMN and to discover changes in information flow in a sample of cognitively normal adults spanning from 48 to 89 years (n = 63). Age was associated to reduced verbal memory performance. Modeling of effective connectivity revealed a pattern of age-related downregulation of posterior DMN regions driven by inhibitory connections from the hippocampus and middle temporal gyrus. Additionally, there was an observed decline in the hippocampus' susceptibility to network inputs with age, effectively disconnecting itself from other regions. The estimated effective connectivity parameters were robust and able to predict the age in out of sample estimates in a leave-one-out cross-validation. Attained education moderated the effects of aging, largely reversing the observed pattern of inhibitory connectivity. Thus, medial prefrontal cortex, hippocampus and posterior DMN regions formed an excitatory cycle of extrinsic connections related to the interaction of age and education. This suggests a compensatory role of years of education in effective connectivity, stressing a possible target for interventions. Our findings suggest a connection to the concept of cognitive reserve, which attributes a protective effect of educational level on cognitive decline in aging (Stern, 2009).

Identification of autism spectrum disorder based on electroencephalography: A systematic review

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and repetitive and stereotyped behaviors. According to the World Health Organization, about 1 in 100 children worldwide has autism. With the global prevalence of ASD, timely and accurate diagnosis has been essential in enhancing the intervention effectiveness for ASD children. Traditional ASD diagnostic methods rely on clinical observations and behavioral assessment, with the disadvantages of time-consuming and lack of objective biological indicators. Therefore, automated diagnostic methods based on machine learning and deep learning technologies have emerged and become significant since they can achieve more objective, efficient, and accurate ASD diagnosis. Electroencephalography (EEG) is an electrophysiological monitoring method that records changes in brain spontaneous potential activity, which is of great significance for identifying ASD children. By analyzing EEG data, it is possible to detect abnormal synchronous neuronal activity of ASD children. This paper gives a comprehensive review of the EEG-based ASD identification using traditional machine learning methods and deep learning approaches, including their merits and potential pitfalls. Additionally, it highlights the challenges and the opportunities ahead in search of more effective and efficient methods to automatically diagnose autism based on EEG signals, which aims to facilitate automated ASD identification.

Functional neuroimaging and behavioral correlates of multisite tDCS as an add-on to language training in a person with post-stroke non-fluent aphasia: a year-long case study

Mary, who experienced non-fluent aphasia as a result of an ischemic stroke, received 10 years of personalized language training (LT), resulting in transient enhancements in speech and comprehension. To enhance these effects, multisite transcranial Direct Current Stimulation (tDCS) was added to her LT regimen for 15 sessions. Assessment using the Reliable Change Index showed that this combination improved her left inferior frontal connectivity and speech production for two months and significantly improved comprehension after one month. The results indicate that using multisite transcranial direct current stimulation (tDCS) can improve the effectiveness of language therapy (LT) for individuals with non-fluent aphasia.

A multi-sample evaluation of the measurement structure and function of the modified monetary incentive delay task in adolescents

Interpreting the neural response elicited during task functional magnetic resonance imaging (fMRI) remains a challenge in neurodevelopmental research. The monetary incentive delay (MID) task is an fMRI reward processing task that is extensively used in the literature. However, modern psychometric tools have not been used to evaluate measurement properties of the MID task fMRI data. The current study uses data for a similar task design across three adolescent samples (N = 346 [Agemean 12.0; 44 % Female]; N = 97 [19.3; 58 %]; N = 112 [20.2; 38 %]) to evaluate multiple measurement properties of fMRI responses on the MID task. Confirmatory factor analysis (CFA) is used to evaluate an a priori theoretical model for the task and its measurement invariance across three samples. Exploratory factor analysis (EFA) is used to identify the data-driven measurement structure across the samples. CFA results suggest that the a priori model is a poor representation of these MID task fMRI data. Across the samples, the data-driven EFA models consistently identify a six-to-seven factor structure with run and bilateral brain region factors. This factor structure is moderately-to-highly congruent across the samples. Altogether, these findings demonstrate a need to evaluate theoretical frameworks for popular fMRI task designs to improve our understanding and interpretation of brain-behavior associations.

Alterations of amygdala volume and functional connectivity in migraine patients comorbid with and without depression

OBJECTIVE

The comorbid relationship between migraine and depression has been well recognized, but its underlying pathophysiology is unclear. Here, we aimed to explore the structural changes of the amygdala and the abnormal functional connectivity of the centromedial amygdala (CMA) in migraineurs with depression.

METHODS

High-resolution T1-weighted and functional magnetic resonance images were acquired from 22 episodic migraineurs with comorbid depression (EMwD), 21 episodic migraineurs without depression (EM), and 17 healthy controls (HC). Voxel-based morphometry and resting-state functional connectivity (rsFC) were applied to examine the intergroup differences in amygdala volume.

RESULTS

The bilateral amygdala volume was increased in the EMwD and EM groups compared with the HC group, but there were no differences between the EMwD and EM groups. The right CMA exhibited decreased rsFC in the left dorsolateral prefrontal cortex (DLPFC) in the EMwD group compared with the EM group, while rsFC increased between the CMA and the contralateral DLPFC in the EM group compared with the HC group. In addition, the EM group showed decreased rsFC between the left CMA and the left pallidum compared with the HC group.

CONCLUSIONS

Enlarged amygdala is an imaging feature of EM and EMwD. The inconsistency of rsFC between CMA and DLPFC between migraineurs with and without depression might indicate that decreased rsFC between CMA and DLPFC is a neuropathologic marker for the comorbidity of migraine and depression. The core regions might be a potential intervention target for the treatment of EMwD in the future.

Category-Selective Representation of Relationships in the Visual Cortex

Understanding social interaction requires processing social agents and their relationships. The latest results show that much of this process is visually solved: visual areas can represent multiple people encoding emergent information about their interaction that is not explained by the response to the individuals alone. A neural signature of this process is an increased response in visual areas, to face-to-face (seemingly interacting) people, relative to people presented as unrelated (back-to-back). This effect highlighted a network of visual areas for representing relational information. How is this network organized? Using functional MRI, we measured the brain activity of healthy female and male humans (N = 42), in response to images of two faces or two (head-blurred) bodies, facing toward or away from each other. Taking the facing > non-facing effect as a signature of relation perception, we found that relations between faces and between bodies were coded in distinct areas, mirroring the categorical representation of faces and bodies in the visual cortex. Additional analyses suggest the existence of a third network encoding relations between (nonsocial) objects. Finally, a separate occipitotemporal network showed the generalization of relational information across body, face, and nonsocial object dyads (multivariate pattern classification analysis), revealing shared properties of relations across categories. In sum, beyond single entities, the visual cortex encodes the relations that bind multiple entities into relationships; it does so in a category-selective fashion, thus respecting a general organizing principle of representation in high-level vision. Visual areas encoding visual relational information can reveal the processing of emergent properties of social (and nonsocial) interaction, which trigger inferential processes.

Alterations in the brain functional network of abstinent male individuals with methamphetamine use disorder

Methamphetamine is a highly addictive psychostimulant drug that is abused globally and is a serious threat to health worldwide. Unfortunately, the specific mechanism underlying addiction remains unclear. Thus, this study aimed to investigate the characteristics of functional connectivity in the brain network and the factors influencing methamphetamine use disorder in patients using magnetic resonance imaging. We included 96 abstinent male participants with methamphetamine use disorder and 46 age- and sex-matched healthy controls for magnetic resonance imaging. Compared with healthy controls, participants with methamphetamine use disorder had greater impulsivity, fewer small-world attributes of the resting-state network, more nodal topological attributes in the cerebellum, greater functional connectivity strength within the cerebellum and between the cerebellum and brain, and decreased frontoparietal functional connectivity strength. In addition, after controlling for covariates, the partial correlation analysis showed that small-world properties were significantly associated with methamphetamine use frequency, psychological craving, and impulsivity. Furthermore, we revealed that the small-word attribute significantly mediated the effect of methamphetamine use frequency on motor impulsivity in the methamphetamine use disorder group. These findings may further improve our understanding of the neural mechanism of impulse control dysfunction underlying methamphetamine addiction and assist in exploring the neuropathological mechanism underlying methamphetamine use disorder-related dysfunction and rehabilitation.

Converging evidence that left extrastriate body area supports visual sensitivity to social interactions

Navigating our complex social world requires processing the interactions we observe. Recent psychophysical and neuroimaging studies provide parallel evidence that the human visual system may be attuned to efficiently perceive dyadic interactions. This work implies, but has not yet demonstrated, that activity in body-selective cortical regions causally supports efficient visual perception of interactions. We adopt a multi-method approach to close this important gap. First, using a large fMRI dataset (n = 92), we found that the left hemisphere extrastriate body area (EBA) responds more to face-to-face than non-facing dyads. Second, we replicated a behavioral marker of visual sensitivity to interactions: categorization of facing dyads is more impaired by inversion than non-facing dyads. Third, in a pre-registered experiment, we used fMRI-guided transcranial magnetic stimulation to show that online stimulation of the left EBA, but not a nearby control region, abolishes this selective inversion effect. Activity in left EBA, thus, causally supports the efficient perception of social interactions.

Effect of Magnetic Resonance Image Quality on Structural and Functional Brain Connectivity: The Maastricht Study

In population-based cohort studies, magnetic resonance imaging (MRI) is vital for examining brain structure and function. Advanced MRI techniques, such as diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI), provide insights into brain connectivity. However, biases in MRI data acquisition and processing can impact brain connectivity measures and their associations with demographic and clinical variables. This study, conducted with 5110 participants from The Maastricht Study, explored the relationship between brain connectivity and various image quality metrics (e.g., signal-to-noise ratio, head motion, and atlas-template mismatches) that were obtained from dMRI and rs-fMRI scans. Results revealed that in particular increased head motion (R2 up to 0.169, p < 0.001) and reduced signal-to-noise ratio (R2 up to 0.013, p < 0.001) negatively impacted structural and functional brain connectivity, respectively. These image quality metrics significantly affected associations of overall brain connectivity with age (up to -59%), sex (up to -25%), and body mass index (BMI) (up to +14%). Associations with diabetes status, educational level, history of cardiovascular disease, and white matter hyperintensities were generally less affected. This emphasizes the potential confounding effects of image quality in large population-based neuroimaging studies on brain connectivity and underscores the importance of accounting for it.

Functional Connectivity Biomarkers in Schizophrenia

Schizophrenia is a debilitating neuropsychiatric disorder that affects approximately 1% of the population and poses a major public health problem. Despite over 100 years of study, the treatment for schizophrenia remains limited, partially due to the lack of knowledge about the neural mechanisms of the illness and how they relate to symptoms. The US Food and Drug Administration (FDA) and the National Institute of Health (NIH) have provided seven biomarker categories that indicate causes, risks, and treatment responses. However, no FDA-approved biomarkers exist for psychiatric conditions, including schizophrenia, highlighting the need for biomarker development. Over three decades, magnetic resonance imaging (MRI)-based studies have identified patterns of abnormal brain function in schizophrenia. By using functional connectivity (FC) data, which gauges how brain regions interact over time, these studies have differentiated patient subgroups, predicted responses to antipsychotic medication, and correlated neural changes with symptoms. This suggests FC metrics could serve as promising biomarkers. Here, we present a selective review of studies leveraging MRI-derived FC to study neural alterations in schizophrenia, discuss how they align with FDA-NIH biomarkers, and outline the challenges and goals for developing FC biomarkers in schizophrenia.

Effect of intracranial electrical stimulation on dynamic functional connectivity in medically refractory epilepsy

Objective

The objective of this study was to explore the distributed network effects of intracranial electrical stimulation in patients with medically refractory epilepsy using dynamic functional connectivity (dFC) and graph indicators.

Methods

The time-varying connectivity patterns of dFC (state-based metrics) as well as topological properties of static functional connectivity (sFC) and dFC (graph indicators) were assessed before and after the intracranial electrical stimulation. The sliding window method and k-means clustering were used for the analysis of dFC states, which were characterized by connectivity strength, occupancy rate, dwell time, and transition. Graph indicators for sFC and dFC were obtained using group statistical tests.

Results

DFCs were clustered into two connectivity configurations: a strongly connected state (state 1) and a sparsely connected state (state 2). After electrical stimulation, the dwell time and occupancy rate of state 1 decreased, while that of state 2 increased. Connectivity strengths of both state 1 and state 2 decreased. For graph indicators, the clustering coefficient, k-core, global efficiency, and local efficiency of patients showed a significant decrease, but the brain networks of patients exhibited higher modularity after electrical stimulation. Especially, for state 1, there was a significant decrease in functional connectivity strength after stimulation within and between the frontal lobe and temporary lobe, both of which are associated with the seizure onset.

Conclusion

Our findings demonstrated that intracranial electrical stimulation significantly changed the time-varying connectivity patterns and graph indicators of the brain in patients with medically refractory epilepsy. Specifically, the electrical stimulation decreased functional connectivity strength in both local-level and global-level networks. This might provide a mechanism of understanding for the distributed network effects of intracranial electrical stimulation and extend the knowledge of the pathophysiological network of medically refractory epilepsy.

Neuroimaging of opioid exposure: a review of preclinical animal models to inform addiction research

Opioid use results in thousands of overdose deaths each year. To address this crisis, we need a better understanding of the neurobiological mechanisms that drive opioid abuse. The noninvasive imaging tools positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and manganese-enhanced magnetic resonance imaging (MEMRI) can be used to identify how brain activity responds to acute opioid exposure and adapts to chronic drug treatment. These techniques can be performed in humans and animal models, and brain networks identified in animals closely map to the human brain. Animal models have the advantage of being able to systematically examine the independent effects of opioid exposure in a controlled environment accounting for the complex factors that drive opioid misuse in humans. This review synthesizes literature that utilized noninvasive neuroimaging tools (PET, fMRI, and MEMRI) measuring brain activity correlates in animals to understand the neurobiological consequences of exposure to abused opioids. A PubMed search in September 2023 identified 25 publications. These manuscripts were divided into 4 categories based on the route and duration of drug exposure (acute/chronic, active/passive administration). Within each category, the results were generally consistent across drug and imaging protocols. These papers cover a 20-year range and highlight the advancements in neuroimaging methodology during that time. These advances have enabled researchers to achieve greater resolution of brain regions altered by opioid exposure and to identify patterns of brain activation across regions (i.e., functional connectivity) and within subregions of structures. After describing the existing literature, we suggest areas where additional research is needed.

Super-resolution QSM in little or no additional time for imaging (NATIve) using 2D EPI imaging in 3 orthogonal planes

Quantitative Susceptibility Mapping has the potential to provide additional insights into neurological diseases but is typically based on a quite long (5-10 min) 3D gradient-echo scan which is highly sensitive to motion. We propose an ultra-fast acquisition based on three orthogonal (sagittal, coronal and axial) 2D simultaneous multi-slice EPI scans with 1 mm in-plane resolution and 3 mm thick slices. Images in each orientation are corrected for susceptibility-related distortions and co-registered with an iterative non-linear Minimum Deformation Averaging (Volgenmodel) approach to generate a high SNR, super-resolution data set with an isotropic resolution of close to 1 mm. The net acquisition time is 3 times the volume acquisition time of EPI or about 12 s, but the three volumes could also replace "dummy scans" in fMRI, making it feasible to acquire QSM in little or No Additional Time for Imaging (NATIve). NATIve QSM values agreed well with reference 3D GRE QSM in the basal ganglia in healthy subjects. In patients with multiple sclerosis, there was also a good agreement between the susceptibility values within lesions and control ROIs and all lesions which could be seen on 3D GRE QSMs could also be visualized on NATIve QSMs. The approach is faster than conventional 3D GRE by a factor of 25-50 and faster than 3D EPI by a factor of 3-5. As a 2D technique, NATIve QSM was shown to be much more robust to motion than the 3D GRE and 3D EPI, opening up the possibility of studying neurological diseases involving iron accumulation and demyelination in patients who find it difficult to lie still for long enough to acquire QSM data with conventional methods.

Exploring the thalamus: a crucial hub for brain function and communication in patients with bulimia nervosa

BACKGROUND

Bulimia nervosa (BN) is an eating disorder characterized by recurrent binge eating and compensatory behaviors. The thalamus plays a crucial role in the neural circuitry related to eating behavior and needs to be further explored in BN.

METHODS

In this study, 49 BN patients and 44 healthy controls (HCs) were recruited. We applied the fractional amplitude of low-frequency fluctuation to investigate regional brain activity in the thalamus and functional connectivity (FC) to examine the synchronization of activity between thalamic subregions and other brain regions in both groups. All results underwent false discovery rate (p < 0.05, FDR correction) correction. Pearson correlation analysis was performed to assess the relationship between the patients' abnormal clinical performance and the thalamic alterations (p < 0.05, FDR correction).

RESULTS

We found no significant differences in neural activity between BN patients and HCs in the sixteen thalamic subregions. However, compared to the HCs, the individuals with BN showed decreased FC between the thalamic subregions and several regions, including the bilateral prefrontal cortex, right inferior parietal lobule, right supplementary motor area, right insula, cingulate gyrus and vermis. Additionally, BN patients showed increased FC between the thalamic subregions and visual association regions, primary sensorimotor cortex, and left cerebellum. These altered FC patterns in the thalamus were found to be correlated with clinical variables (the frequency of binge eating/purging per week and external eating behavior scale scores) in the BN group. All results have passed FDR correction.

CONCLUSIONS

Our study provides evidence that there is disrupted FC between thalamic subregions and other brain regions in BN patients during resting state. These regions are primarily located within the frontoparietal network, default mode network, somatosensory, and visual network. These findings elucidate the neural activity characteristics underlying BN and suggest that thalamic subregions have potential as targets for future neuromodulation interventions.

Fronto-temporal dysfunction in appetitive regulation of bulimia nervosa with affective disorders: A regional homogeneity and remote connectivity pattern analysis

OBJECTIVE

The aim of this study was to assess brain functional alterations in BN patients with affective disorders and their association with maladaptive eating behaviors.

METHODS

A total of 42 BN patients with affective disorders (anxiety and depression) and 47 healthy controls (HCs) were enrolled in this study. The resting-state fMRI data were analyzed for functional changes as indicated by regional homogeneity based on Kendall's coefficient of concordance (KCC-ReHo) and seed-based functional connectivity (FC). A principal component analysis (PCA) model was used to identify the commonalities within the behavioral questionnaires from the BN group.

RESULTS

Patients in the BN group showed decreased ReHo in the bilateral middle frontal gyrus (MFG) and right supramarginal gyrus (SMG). Additionally, the BN group showed increased FC between the left MFG and the right inferior temporal gyrus (ITG); decreased FC between the right MFG and the bilateral insula and the left middle temporal gyrus (MTG); and decreased FC between the right SMG and the left superior temporal gyrus (STG) and right inferior frontal gyrus (IFG). In the FC-behavior association analysis, the second principal component (PC2) was negatively correlated with FC between the left MFG and the right ITG.

CONCLUSION

Based on a brain functional analysis (ReHo and FC), this study revealed significant aberrant changes in the frontal-temporal regions of BN patients with affective disorders. These regions, which serve as fronto-temporal circuitry, are associated with restraint and emotional eating behaviors. Our findings shed new light on the neural mechanisms underlying the condition.

Lower Dorsal Lateral Prefrontal Cortex Functional Connectivity in Late-Life Depression With Suicidal Ideation

OBJECTIVE

The dorsal lateral prefrontal cortex (DLPFC) has been identified as a neuromodulation target for alleviating suicidal ideation. Dysfunctional DLPFC has been implicated in suicidality in depression. This study aimed to investigate the functional connectivity (FC) of the DLPFC in late-life depression (LLD) with suicidal ideation.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) data from 32 LLD patients with suicidal ideation (LLD-S), 41 LLD patients without suicidal ideation (LLD-NS), and 54 healthy older adults (HOA) were analyzed using DLPFC seed-based FC analyses. Group differences in FC were examined, and machine learning was applied to explore the potential of DLPFC-FC for classifying LLD-S from LLD-NS.

RESULTS

Abnormal DLPFC-FC patterns were observed in LLD-S, characterized by lower connectivity with the angular gyrus, precuneus, and superior frontal gyrus compared to LLD-NS and healthy controls. A classification model based on the identified DLPFC-FC achieved an accuracy of 75%.

CONCLUSION

The lower FC of DLPFC networks may contribute to the neurobiological mechanism of suicidal ideation in late-life depression. These findings may facilitate suicide prevention for LLD by providing potential neuroimaging markers and network-based neuromodulation targets. However, further confirmation with larger sample sizes and experimental designs is warranted.

Imagining the future of optical microscopy: everything, everywhere, all at once

The optical microscope has revolutionized biology since at least the 17th Century. Since then, it has progressed from a largely observational tool to a powerful bioanalytical platform. However, realizing its full potential to study live specimens is hindered by a daunting array of technical challenges. Here, we delve into the current state of live imaging to explore the barriers that must be overcome and the possibilities that lie ahead. We venture to envision a future where we can visualize and study everything, everywhere, all at once - from the intricate inner workings of a single cell to the dynamic interplay across entire organisms, and a world where scientists could access the necessary microscopy technologies anywhere.

Enhanced brain functional connectivity and activation after 12-week Tai Chi-based action observation training in patients with Parkinson's disease

Introduction

Motor-cognitive interactive interventions, such as action observation training (AOT), have shown great potential in restoring cognitive function and motor behaviors. It is expected that an advanced AOT incorporating specific Tai Chi movements with continuous and spiral characteristics can facilitate the shift from automatic to intentional actions and thus enhance motor control ability for early-stage PD. Nonetheless, the underlying neural mechanisms remain unclear. The study aimed to investigate changes in brain functional connectivity (FC) and clinical improvement after 12 weeks of Tai Chi-based action observation training (TC-AOT) compared to traditional physical therapy (TPT).

Methods

Thirty early-stage PD patients were recruited and randomly assigned to the TC-AOT group (N = 15) or TPT group (N = 15). All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans before and after 12 weeks of training and clinical assessments. The FCs were evaluated by seed-based correlation analysis based on the default mode network (DMN). The rehabilitation effects of the two training methods were compared while the correlations between significant FC changes and clinical improvement were investigated.

Results

The results showed that the TC-AOT group exhibited significantly increased FCs between the dorsal medial prefrontal cortex and cerebellum crus I, between the posterior inferior parietal lobe and supramarginal gyrus, and between the temporal parietal junction and clusters of middle occipital gyrus and superior temporal. Moreover, these FC changes had a positive relationship with patients' improved motor and cognitive performance.

Discussion

The finding supported that the TC-AOT promotes early-stage PD rehabilitation outcomes by promoting brain neuroplasticity where the FCs involved in the integration of sensorimotor processing and motor learning were strengthened.

Connectivity differences between inferior frontal gyrus and mentalizing network in autism as compared to developmental coordination disorder and non-autistic youth

Prior studies have compared neural connectivity during mentalizing tasks in autism (ASD) to non-autistic individuals and found reduced connectivity between the inferior frontal gyrus (IFG) and mentalizing regions. However, given that the IFG is involved in motor processing, and about 80% of autistic individuals have motor-related difficulties, it is necessary to explore if these differences are specific to ASD or instead similar across other developmental motor disorders, such as developmental coordination disorder (DCD). Participants (29 ASD, 20 DCD, 31 typically developing [TD]; ages 8-17) completed a mentalizing task in the fMRI scanner, where they were asked to think about why someone was performing an action. Results indicated that the ASD group, as compared to both TD and DCD groups, showed significant functional connectivity differences when mentalizing about other's actions. The left IFG seed revealed ASD connectivity differences with the: bilateral temporoparietal junction (TPJ), left insular cortex, and bilateral dorsolateral prefrontal cortex (DLPFC). Connectivity differences using the right IFG seed revealed ASD differences in the: left insula, and right DLPFC. These results indicate that connectivity differences between the IFG, mentalizing regions, emotion and motor processing regions are specific to ASD and not a result of potentially co-occurring motor differences.

The Impact of Mindfulness on Functional Brain Connectivity and Peripheral Inflammation in Breast Cancer Survivors with Cognitive Complaints

BACKGROUND

Cancer-related cognitive impairment (CRCI) has been linked to functional brain changes and inflammatory processes. Hence, interventions targeting these underlying mechanisms are needed. In this study, we investigated the effects of a mindfulness-based intervention on brain function and inflammatory profiles in breast cancer survivors with CRCI.

METHODS

Female breast cancer survivors reporting cognitive complaints (n = 117) were randomly assigned to a mindfulness-based intervention (n = 43), physical training (n = 36), or waitlist control condition (n = 38). Region-of-interest (ROI) and graph theory analyses of resting state functional MRI data were performed to study longitudinal group differences in functional connectivity and organization in the default mode, dorsal attention, salience, and frontoparietal network. Additionally, bead-based immunoassays were used to investigate the differences in inflammatory profiles on serum samples. Measures were collected before, immediately after and three months post-intervention.

RESULTS

No ROI-to-ROI functional connectivity changes were identified. Compared to no intervention, graph analysis showed a larger decrease in clustering coefficient after mindfulness and physical training. Additionally, a larger increase in global efficiency after physical training was identified. Furthermore, the physical training group showed a larger decrease in an inflammatory profile compared to no intervention (IL-12p70, IFN-γ, IL-1β, and IL-8).

CONCLUSION

Both mindfulness and physical training induced changes in the functional organization of networks related to attention, emotion processing, and executive functioning. While both interventions reduced functional segregation, only physical training increased functional integration of the neural network. In conclusion, physical training had the most pronounced effects on functional network organization and biomarkers of inflammation, two mechanisms that might be involved in CRCI.

Three potential neurovascular pathways driving the benefits of mindfulness meditation for older adults

Mindfulness meditation has been shown to be beneficial for a range of different health conditions, impacts brain function and structure relatively quickly, and has shown promise with aging samples. Functional magnetic resonance imaging metrics provide insight into neurovascular health which plays a key role in both normal and pathological aging processes. Experimental mindfulness meditation studies that included functional magnetic resonance metrics as an outcome measure may point to potential neurovascular mechanisms of action relevant for aging adults that have not yet been previously examined. We first review the resting-state magnetic resonance studies conducted in exclusively older adult age samples. Findings from older adult-only samples are then used to frame the findings of task magnetic resonance imaging studies conducted in both clinical and healthy adult samples. Based on the resting-state studies in older adults and the task magnetic resonance studies in adult samples, we propose three potential mechanisms by which mindfulness meditation may offer a neurovascular therapeutic benefit for older adults: (1) a direct neurovascular mechanism via increased resting-state cerebral blood flow; (2) an indirect anti-neuroinflammatory mechanism via increased functional connectivity within the default mode network, and (3) a top-down control mechanism that likely reflects both a direct and an indirect neurovascular pathway.

Coffee consumption decreases the connectivity of the posterior Default Mode Network (DMN) at rest

Habitual coffee consumers justify their life choices by arguing that they become more alert and increase motor and cognitive performance and efficiency; however, these subjective impressions still do not have a neurobiological correlation. Using functional connectivity approaches to study resting-state fMRI data in a group of habitual coffee drinkers, we herein show that coffee consumption decreased connectivity of the posterior default mode network (DMN) and between the somatosensory/motor networks and the prefrontal cortex, while the connectivity in nodes of the higher visual and the right executive control network (RECN) is increased after drinking coffee; data also show that caffeine intake only replicated the impact of coffee on the posterior DMN, thus disentangling the neurochemical effects of caffeine from the experience of having a coffee.

BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer's disease

Recent advancements in AI, big data analytics, and magnetic resonance imaging (MRI) have revolutionized the study of brain diseases such as Alzheimer's Disease (AD). However, most AI models used for neuroimaging classification tasks have limitations in their learning strategies, that is batch training without the incremental learning capability. To address such limitations, the systematic Brain Informatics methodology is reconsidered to realize evidence combination and fusion computing with multi-modal neuroimaging data through continuous learning. Specifically, we introduce the BNLoop-GAN (Loop-based Generative Adversarial Network for Brain Network) model, utilizing multiple techniques such as conditional generation, patch-based discrimination, and Wasserstein gradient penalty to learn the implicit distribution of brain networks. Moreover, a multiple-loop-learning algorithm is developed to combine evidence with better sample contribution ranking during training processes. The effectiveness of our approach is demonstrated through a case study on the classification of individuals with AD and healthy control groups using various experimental design strategies and multi-modal brain networks. The BNLoop-GAN model with multi-modal brain networks and multiple-loop-learning can improve classification performance.

Network targets for therapeutic brain stimulation: towards personalized therapy for pain

Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain.

Relationships Among Short Self-Reported Sleep Duration, Cognitive Impairment, and Insular Functional Connectivity in Late-Life Depression

BACKGROUND

Both late-life depression (LLD) and short sleep duration increase the risk of cognitive impairment. Increased insular resting-state functional connectivity (FC) has been reported in individuals with short sleep duration and dementia.

OBJECTIVE

This study aimed to investigate whether short sleep duration is associated with impaired cognition and higher insular FC in patients with LLD.

METHODS

This case- control study recruited 186 patients with LLD and 83 normal controls (NC), and comprehensive psychometric assessments, sleep duration reports and resting-state functional MRI scans (81 LLD patients and 54 NC) were conducted.

RESULTS

Patients with LLD and short sleep duration (LLD-SS patients) exhibited more severe depressive symptoms and worse cognitive function than those with normal sleep duration (LLD-NS patients) and NC. LLD-SS patients exhibited higher FC between the bilateral insula and inferior frontal gyrus (IFG) pars triangularis than LLD-NS patients and NC, while LLD-NS patients exhibited lower FC than NC. Increased insular FC was correlated with short sleep duration, severe depressive symptoms, and slower information processing speeds. Furthermore, an additive effect was found between sleep duration and LLD on global cognition and insular FC.

CONCLUSION

LLD-SS patients exhibited impaired cognition and increased insular FC. Abnormal FC in LLD-SS patients may be a therapeutic target for neuromodulation to improve sleep and cognitive performance and thus decrease the risk of dementia.

External Hardware and Sensors, for Improved MRI

Complex engineered systems are often equipped with suites of sensors and ancillary devices that monitor their performance and maintenance needs. MRI scanners are no different in this regard. Some of the ancillary devices available to support MRI equipment, the ones of particular interest here, have the distinction of actually participating in the image acquisition process itself. Most commonly, such devices are used to monitor physiological motion or variations in the scanner's imaging fields, allowing the imaging and/or reconstruction process to adapt as imaging conditions change. "Classic" examples include electrocardiography (ECG) leads and respiratory bellows to monitor cardiac and respiratory motion, which have been standard equipment in scan rooms since the early days of MRI. Since then, many additional sensors and devices have been proposed to support MRI acquisitions. The main physical properties that they measure may be primarily "mechanical" (eg acceleration, speed, and torque), "acoustic" (sound and ultrasound), "optical" (light and infrared), or "electromagnetic" in nature. A review of these ancillary devices, as currently available in clinical and research settings, is presented here. In our opinion, these devices are not in competition with each other: as long as they provide useful and unique information, do not interfere with each other and are not prohibitively cumbersome to use, they might find their proper place in future suites of sensors. In time, MRI acquisitions will likely include a plurality of complementary signals. A little like the microbiome that provides genetic diversity to organisms, these devices can provide signal diversity to MRI acquisitions and enrich measurements. Machine-learning (ML) algorithms are well suited at combining diverse input signals toward coherent outputs, and they could make use of all such information toward improved MRI capabilities. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Multimodal Brain MRI of Deep Gray Matter Changes Associated With Inflammatory Bowel Disease

BACKGROUND

Behavioral symptoms, including mood disorders, substantially impact the quality of life of patients with inflammatory bowel disease (IBD), even when clinical remission is achieved. Here, we used multimodal magnetic resonance imaging (MRI) to determine if IBD is associated with changes in the structure and function of deep gray matter brain regions that regulate and integrate emotional, cognitive, and stress responses.

METHODS

Thirty-five patients with ulcerative colitis (UC) or Crohn's disease (CD) and 32 healthy controls underwent 3 Tesla MRIs to assess volume, neural activity, functional connection strength (connectivity), inflammation, and neurodegeneration of key deep gray matter brain regions (thalamus, caudate, pallidum, putamen, amygdala, hippocampus, and hypothalamus) involved in emotional, cognitive and stress processing. Associations with sex, presence of pain, disease activity, and C-reactive protein (CRP) concentration were examined.

RESULTS

Significantly increased activity and functional connectivity were observed in cognitive and emotional processing brain regions, including parts of the limbic system, basal ganglia, and hypothalamus of IBD patients compared with healthy controls. Inflammatory bowel disease patients exhibited significantly increased volumes of the amygdala and hypothalamus, as well as evidence of neurodegeneration in the putamen and pallidum. Hippocampal neural activity was increased in IBD patients with active disease. The volume of the thalamus was positively correlated with CRP concentration and was increased in females experiencing pain.

CONCLUSIONS

Patients with IBD exhibit functional and structural changes in the limbic and striatal systems. These changes may be targets for assessing or predicting the response to therapeutic interventions aimed at improving comorbid emotional and cognitive symptoms.

Brain functional activity of swallowing: A meta-analysis of functional magnetic resonance imaging

BACKGROUND

Swallowing is one of the most important activities in our life and serves the dual roles of nutritional intake and eating enjoyment.

OBJECTIVE

The study aimed to conduct a meta-analysis to investigate the brain activity of swallowing.

METHODS

Studies of swallowing using functional magnetic resonance imaging were reviewed in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Chinese Science and Technology Periodical Database (VIP) and Wan Fang before 30 November 2021. Two authors analysed the studies for eligibility criteria. The final inclusion of studies was decided by consensus. An activation likelihood estimation (ALE) meta-analysis of these studies was performed with GingerALE, including 16 studies.

RESULTS

For swallowing, clusters with high activation likelihood were found in the bilateral insula, bilateral pre-central gyrus, bilateral post-central gyrus, left transverse temporal gyrus, right medial front gyrus, bilateral inferior frontal gyrus and bilateral cingulate gyrus. For water swallowing, clusters with high activation likelihood were found in the bilateral inferior frontal gyrus and the left pre-central gyrus. For saliva swallowing, clusters with high activation likelihood were found in the bilateral cingulate gyrus, bilateral pre-central gyrus, left post-central gyrus and left transverse gyrus.

CONCLUSION

This meta-analysis reflects that swallowing is regulated by both sensory and motor cortex, and saliva swallowing activates more brain areas than water swallowing, which would promote our knowledge of swallowing and provide some direction for clinical and other research.

Effect and cerebral mechanism of moxibustion at heat-sensitized Yaoyangguan (GV3) in patients with lumbar disc herniation and myofascial pain syndrome by resting-state functionality magnetic resonance imaging: protocol for an observational study

We want to explore the analgesic brain effect of the moxibustion at heat-sensitized Yaoyangguan (GV3) in patients with lumbar disc herniation (LDH) and myofascial pain syndrome (MPS). In an assessor-blinded observational study, we will include 15 LDH and 15 MPS. They will accept same treatment of heat-sensitive moxibustion at Yaoyangguan (GV3). The resting-state functionality magnetic resonance imaging image data of brain activities before and after treatment will be analyzed by mean fractional amplitude of low-frequency fluctuation, regional homogeneity analysis and brain functional connection. We select seed of first sensory cortex, second sensory cortex, insula cortex, periaqueductal gray and anterior cingulate cortex as the regions of interest to analyse the relationship between brain functional connectivity of pain-related networks and clinical data. Our study could disclose key brain targets and central response characteristics of the analgesic brain effect and the brain functional connection of heat-sensitive moxibustion.

Anticipatory cues in emotional processing shift the activation of a combined salience sensorimotor functional network in drug-naïve depressed patients

BACKGROUND

Major depressive disorder is characterized by a large-scale brain network dysfunction, contributing to impairments in cognitive and affective functioning. Core regions of default mode, limbic and salience networks are also impaired in emotional processing and anticipation. This study aimed to explore default mode, salience, and limbic networks modulation during the processing of emotional stimuli with and without anticipatory cues in depression, and further investigate how these networks were functionally coupled with the rest of the brain.

METHODS

Twenty-one drug-naïve depressed patients and 15 matched controls were included in the study. All participants completed a psychological assessment and the affective pictures paradigm during an fMRI acquisition. Group independent component analysis and psychophysiological interactions analyses were performed.

RESULTS

A significant interaction between Cue, Valence and Group was found for the salience/sensorimotor network. When processing uncued emotional stimuli, patients showed increased activation of this network for negative vs. neutral pictures, whereas when anticipatory cues were displayed previously to the picture presentation, they invert this pattern of activation (hyperactivating the salience/sensorimotor network for positive vs. neutral pictures). Patients showed increased functional connectivity between the salience/sensorimotor network and the left amygdala as well as the right inferior parietal lobule compared to controls when processing uncued negative pictures.

LIMITATIONS

The sample size was modest, and the salience/sensorimotor network included regions not typically identified as part of salience network. Thus, this study should be replicated to further interpret the results.

CONCLUSIONS

Anticipatory cues shift the pattern of activation of the salience/sensorimotor network in drug-naïve depressed patients.