Data-Driven and Predefined ROI-Based Quantification of Long-Term Resting-State fMRI Reproducibility

Investigating resting-state functional connectivity changes within procedural memory network across neuropsychiatric disorders using fMRI

BACKGROUND

Cognitive networks impairments are common in neuropsychiatric disorders like Attention Deficit Hyperactivity Disorder (ADHD), bipolar disorder (BD), and schizophrenia (SZ). While previous research has focused on specific brain regions, the role of the procedural memory as a type of long-term memory to examine cognitive networks impairments in these disorders remains unclear. This study investigates alterations in resting-state functional connectivity (rs-FC) within the procedural memory network to explore brain function associated with cognitive networks in patients with these disorders.

METHODS

This study analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 40 individuals with ADHD, 49 with BD, 50 with SZ, and 50 healthy controls (HCs). A procedural memory network was defined based on the selection of 34 regions of interest (ROIs) associated with the network in the Harvard-Oxford Cortical Structural Atlas (default atlas). Multivariate region of interest to region of interest connectivity (mRRC) was used to analyze the rs-FC between the defined network regions. Significant differences in rs-FC between patients and HCs were identified (P < 0.001).

RESULTS

ADHD patients showed increased Cereb45 l - Cereb3 r rs-FC (p = 0.000067) and decreased Cereb1 l - Cereb6 l rs-FC (p = 0.00092). BD patients exhibited increased rs-FC between multiple regions, including Claustrum r - Caudate r (p = 0.00058), subthalamic nucleus r - Pallidum l (p = 0.00060), substantia nigra l - Cereb2 l (p = 0.00082), Cereb10 r - SMA r (p = 0.00086), and Cereb9 r - SMA l (p = 0.00093) as well as decreased rs-FC in subthalamic nucleus r - Cereb6 l (p = 0.00013) and Cereb9 r - Cereb9 l (p = 0.00033). SZ patients indicated increased Caudate r- putamen l rs-FC (p = 0.00057) and decreased rs-FC in subthalamic nucleus r - Cereb6 l (p = 0.000063), and Cereb1 r - subthalamic nucleus r (p = 0.00063).

CONCLUSIONS

This study found significant alterations in rs-FC within the procedural memory network in patients with ADHD, BD, and SZ compared to HCs. These findings suggest that disrupted rs-FC within this network may related to cognitive networks impairments observed in these disorders.

CLINICAL TRIAL NUMBER

Not applicable.

An improved spectral clustering method for accurate detection of brain resting-state networks

This paper proposes a data-driven analysis method to accurately partition large-scale resting-state functional brain networks from fMRI data. The method is based on a spectral clustering algorithm and combines eigenvector direction selection with Pearson correlation clustering in the spectral space. The method is an improvement on available spectral clustering methods, capable of robustly identifying active brain networks consistent with those from model-driven methods at different noise levels, even at the noise level of real fMRI data.

Associations between differential connectivity patterns of executive control networks and APOE ɛ4 in the Alzheimer continuum

The APOE ɛ4 allele and age are risk factors for Alzheimer's disease (AD) and contribute to decreased executive function. However, the influence of APOE ɛ4 on the executive control network (ECN) in the AD continuum is still unclear. This study included 269 participants aged between 50 and 95 years old, based on ADNI data, including 104 cognitively normal (CN) individuals, 72 individuals with early mild cognitive impairment (EMCI), 55 individuals with late mild cognitive impairment (LMCI), and 38 AD patients. Within each disease group, participants were subdivided into APOE ɛ4 carriers and non-carriers. We explored brain regions within the ECN affected by the interactions between genes and disease states by resting-state functional magnetic resonance imaging (fMRI) and voxel-based two-way analysis of variance (ANOVA). Subsequently, functional connectivity (FC) between seeds and peak clusters were extracted and correlated with the cognitive performance. We found that the damages of carrying APOE ɛ4 in ECNs mainly distributed in the fronto-parietal and parietal-temporal systems. Functional network intergroup differences indicated increased intrafrontal and fronto-parietal connectivity at the early stage of AD and increased connectivity between the parietal lobe and related regions at late disease in these APOE ɛ4 carriers. Our conclusion is that the functional connectivity in the ECN exhibits different distinguishably patterns of impairment in the AD continuum under the influence of the APOE ɛ4 allele. Patients with different genotypes showed heterogeneity in functional network changes in the early stages of disease, which may be a potential biomarker for early AD.

Multi-Task Learning and Sparse Discriminant Canonical Correlation Analysis for Identification of Diagnosis-Specific Genotype-Phenotype Association

The primary objective of imaging genetics research is to investigate the complex genotype-phenotype association for the disease under study. For example, to understand the impact of genetic variations over the brain functions and structure, the genotypic data such as single nucleotide polymorphism (SNP) is integrated with the phenotypic data such as imaging quantitative traits. The sparse models, based on canonical correlation analysis (CCA), are popular in this area to find the complex bi-multivariate genotype-phenotype association, as the number of features in genotypic and/or phenotypic data is significantly higher as compared to the number of samples. However, the sparse CCA based methods are, in general, unsupervised in nature, and fail to identify the diagnose-specific features those play an important role for the diagnosis and prognosis of the disease under study. In this regard, a new supervised model is proposed to study the complex genotype-phenotype association, by judiciously integrating the merits of CCA, linear discriminant analysis (LDA) and multi-task learning. The proposed model can identify the diagnose-specific as well as the diagnose-consistent features with significantly lower computational complexity. The performance of the proposed method, along with a comparison with the state-of-the-art methods, is evaluated on several synthetic data sets and one real imaging genetics data collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. In the current study, the SNP as genetic data and resting state functional MRI ( fMRI) as imaging data are integrated to find the complex genotype-phenotype association. An important finding is that the proposed method has better correlation value, improved noise resistance and stability, and also has better feature selection ability. All the results illustrate the power and capability of the proposed method to find the diagnostic group-specific imaging genetic association, which may help to understand the neurodegenerative disorder in a more comprehensive way.

Longitudinal changes in resting-state functional connectivity as markers of vulnerability or resilience in first-degree relatives of patients with bipolar disorder

BACKGROUND

There is a significant contribution of genetic factors to the etiology of bipolar disorder (BD). Unaffected first-degree relatives of patients (UR) with BD are at increased risk of developing mental disorders and may manifest cognitive impairments and alterations in brain functional and connective dynamics, akin to their affected relatives.

METHODS

In this prospective longitudinal study, resting-state functional connectivity was used to explore stable and progressive markers of vulnerability i.e. abnormalities shared between UR and BD compared to healthy controls (HC) and resilience i.e. features unique to UR compared to HC and BD in full or partial remission (UR n = 72, mean age = 28.0 ± 7.2 years; HC n = 64, mean age = 30.0 ± 9.7 years; BD patients n = 91, mean age = 30.6 ± 7.7 years). Out of these, 34 UR, 48 BD, and 38 HC were investigated again following a mean time of 1.3 ± 0.4 years.

RESULTS

At baseline, the UR showed lower connectivity values within the default mode network (DMN), frontoparietal network, and the salience network (SN) compared to HC. This connectivity pattern in UR remained stable over the follow-up period and was not present in BD, suggesting a resilience trait. The UR further demonstrated less negative connectivity between the DMN and SN compared to HC, abnormality that remained stable over time and was also present in BD, suggesting a vulnerability marker.

CONCLUSION

Our findings indicate the coexistence of both vulnerability-related abnormalities in resting-state connectivity, as well as adaptive changes possibly promoting resilience to psychopathology in individual at familial risk.

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.

Effect of scanning duration and sample size on reliability in resting state fMRI dynamic causal modeling analysis

Despite its widespread use, resting-state functional magnetic resonance imaging (rsfMRI) has been criticized for low test-retest reliability. To improve reliability, researchers have recommended using extended scanning durations, increased sample size, and advanced brain connectivity techniques. However, longer scanning runs and larger sample sizes may come with practical challenges and burdens, especially in rare populations. Here we tested if an advanced brain connectivity technique, dynamic causal modeling (DCM), can improve reliability of fMRI effective connectivity (EC) metrics to acceptable levels without extremely long run durations or extremely large samples. Specifically, we employed DCM for EC analysis on rsfMRI data from the Human Connectome Project. To avoid bias, we assessed four distinct DCMs and gradually increased sample sizes in a randomized manner across ten permutations. We employed pseudo true positive and pseudo false positive rates to assess the efficacy of shorter run durations (3.6, 7.2, 10.8, 14.4 min) in replicating the outcomes of the longest scanning duration (28.8 min) when the sample size was fixed at the largest (n = 160 subjects). Similarly, we assessed the efficacy of smaller sample sizes (n = 10, 20, …, 150 subjects) in replicating the outcomes of the largest sample (n = 160 subjects) when the scanning duration was fixed at the longest (28.8 min). Our results revealed that the pseudo false positive rate was below 0.05 for all the analyses. After the scanning duration reached 10.8 min, which yielded a pseudo true positive rate of 92%, further extensions in run time showed no improvements in pseudo true positive rate. Expanding the sample size led to enhanced pseudo true positive rate outcomes, with a plateau at n = 70 subjects for the targeted top one-half of the largest ECs in the reference sample, regardless of whether the longest run duration (28.8 min) or the viable run duration (10.8 min) was employed. Encouragingly, smaller sample sizes exhibited pseudo true positive rates of approximately 80% for n = 20, and 90% for n = 40 subjects. These data suggest that advanced DCM analysis may be a viable option to attain reliable metrics of EC when larger sample sizes or run times are not feasible.

Craving dynamics and related cerebral substrates predict timing of use in alcohol, tobacco, and cannabis use disorders

Background

Patients treated for Substance Use Disorders exhibit highly fluctuating patterns of craving that could reveal novel prognostic markers of use. Accordingly, we 1) measured fluctuations within intensively repeated measures of craving and 2) linked fluctuations of craving to connectivity indices within resting-state (rs) brain regions to assess their relation to use among patients undergoing treatment for Alcohol, Tobacco and Cannabis Use Disorders.

Method

Participants -64 individuals with SUD for tobacco, alcohol, or cannabis and 35 healthy controls-completed a week of Ecological Momentary Assessment (EMA) during which they reported craving intensity and substance use five times daily. Before EMA, a subsample of 50 patients, and 34 healthy controls also completed resting-state (rs)-MRI acquisitions. Craving temporal dynamics within each day were characterized using Standard Deviation (SD), Auto-Correlation Factor (ACF), and Mean Successive Square Difference (MSSD). Absolute Difference (AD) in craving between assessments was a prospective prediction measure.

Results

Within-day, higher MSSD predicted greater substance use while controlling for mean craving. Prospectively higher AD predicted later increased substance use independently of previous use or craving level. Moreover, MSSD was linked to strength in five functional neural connections, most involving frontotemporal systems. Cerebello-thalamic and thalamo-frontal connectivity were also linked to substance use and distinguished the SUD from the controls.

Conclusion

To the best of our knowledge, this is the first study to indicate that instability in craving may be a trigger for use in several SUD types, beyond the known effect of craving intensity.

Brain connectivity under light sedation with midazolam and ketamine during task performance and the periodic experience of pain: Examining concordance between different approaches for seed-based connectivity analysis

This work focused on functional connectivity changes under midazolam and ketamine sedation during performance of a memory task, with the periodic experience of pain. To maximize ability to compare to previous and future work, we performed secondary region of interest (ROI)-to-ROI functional connectivity analyses on these data, using two granularities of scale for ROIs. These findings are compared to the results of a previous seed-to-voxel analysis methodology, employed in the primary analysis. Healthy adult volunteers participated in this randomized crossover 3 T functional MRI study under no drug, followed by subanesthetic doses of midazolam or ketamine achieving minimal sedation. Periodic painful stimulation was delivered while subjects repeatedly performed a memory-encoding task. Atlas-based and network-level ROIs were used from within Conn Toolbox (ver 18). Timing of experimental task events was regressed from the data to assess drug-induced changes in background connectivity, using ROI-to-ROI methodology. Compared to saline, ROI-to-ROI connectivity changes under ketamine did not survive correction for multiple comparisons, thus data presented is from 16 subjects in a paired analysis between saline and midazolam. In both ROI-to-ROI analyses, the predominant direction of change was towards increased connectivity under midazolam, compared to saline. These connectivity increases occurred between functionally-distinct brain areas, with a posterior-predominant spatial distribution that included many long-range connectivity changes. During performance of an experimental task that involved periodic painful stimulation, compared to saline, low-dose midazolam was associated with robust increases in functional connectivity. This finding was concordant across different seed-based analyses for midazolam, but not ketamine. The neuroimaging drug trial from which this data was drawn was pre-registered (NCT-02515890) prior to enrollment of the first subject.

Classification of human chronotype based on fMRI network-based statistics

Chronotype-the relationship between the internal circadian physiology of an individual and the external 24-h light-dark cycle-is increasingly implicated in mental health and cognition. Individuals presenting with a late chronotype have an increased likelihood of developing depression, and can display reduced cognitive performance during the societal 9-5 day. However, the interplay between physiological rhythms and the brain networks that underpin cognition and mental health is not well-understood. To address this issue, we use rs-fMRI collected from 16 people with an early chronotype and 22 people with a late chronotype over three scanning sessions. We develop a classification framework utilizing the Network Based-Statistic methodology, to understand if differentiable information about chronotype is embedded in functional brain networks and how this changes throughout the day. We find evidence of subnetworks throughout the day that differ between extreme chronotypes such that high accuracy can occur, describe rigorous threshold criteria for achieving 97.3% accuracy in the Evening and investigate how the same conditions hinder accuracy for other scanning sessions. Revealing differences in functional brain networks based on extreme chronotype suggests future avenues of research that may ultimately better characterize the relationship between internal physiology, external perturbations, brain networks, and disease.

Unbalanced fronto-pallidal neurocircuit underlying set shifting in obsessive-compulsive disorder

Maladaptive habitual behaviours of obsessive-compulsive disorder are characterized by cognitive inflexibility, which hypothetically arises from dysfunctions of a certain cortico-basal ganglia-thalamo-cortical circuit including the ventrolateral prefrontal region. Inside this neurocircuit, an imbalance between distinct striatal projections to basal ganglia output nuclei, either directly or indirectly via the external globus pallidus, is suggested to be relevant for impaired arbitration between facilitation and inhibition of cortically initiated activity. However, current evidence of individually altered cortico-striatal or thalamo-cortical connectivities is insufficient to understand how cortical dysconnections are linked to the imbalanced basal ganglia system in patients. In this study, we aimed to identify aberrant ventrolateral prefronto-basal ganglia-thalamic subnetworks representing direct-indirect imbalance and its association with cognitive inflexibility in patients. To increase network detection sensitivity, we constructed a cortico-basal ganglia-thalamo-cortical network model incorporating striatal, pallidal and thalamic subregions defined by unsupervised clustering in 105 medication-free patients with obsessive-compulsive disorder (age = 25.05 ± 6.55 years, male/female = 70/35) and 99 healthy controls (age = 23.93 ± 5.80 years, male/female = 64/35). By using the network-based statistic method, we analysed group differences in subnetworks formed by suprathreshold dysconnectivities. Using linear regression models, we tested subnetwork dysconnectivity effects on symptom severity and set-shifting performance assessed by well-validated clinical and cognitive tests. Compared with the healthy controls, patients were slower to track the Part B sequence of the Trail Making Test when the effects of psychomotor and visuospatial functions were adjusted (t = 3.89, P < 0.001) and made more extradimensional shift errors (t = 4.09, P < 0.001). In addition to reduced fronto-striatal and striato-external pallidal connectivities and hypoconnected striato-thalamic subnetwork [P = 0.001, family-wise error rate (FWER) corrected], patients had hyperconnected fronto-external pallidal (P = 0.012, FWER corrected) and intra-thalamic (P = 0.015, FWER corrected) subnetworks compared with the healthy controls. Among the patients, the fronto-pallidal subnetwork alteration, especially ventrolateral prefronto-external globus pallidal hyperconnectivity, was associated with relatively fewer extradimensional shifting errors (β = -0.30, P = 0.001). Our findings suggest that the hyperconnected fronto-external pallidal subnetwork may have an opposite effect to the imbalance caused by the reduced indirect pathway (fronto-striato-external pallidal) connectivities in patients. This ventrolateral prefrontal hyperconnectivity may help the external globus pallidus disinhibit basal ganglia output nuclei, which results in behavioural inhibition, so as to compensate for the impaired set shifting. We suggest the ventrolateral prefrontal and external globus pallidus as neuromodulatory targets for inflexible habitual behaviours in obsessive-compulsive disorder.

Test-retest stability of spontaneous brain activity and functional connectivity in the core resting-state networks assessed with ultrahigh field 7-Tesla resting-state functional magnetic resonance imaging

The growing demand for precise and reliable biomarkers in psychiatry is fueling research interest in the hope that identifying quantifiable indicators will improve diagnoses and treatment planning across a range of mental health conditions. The individual properties of brain networks at rest have been highlighted as a possible source for such biomarkers, with the added advantage that they are relatively straightforward to obtain. However, an important prerequisite for their consideration is their reproducibility. While the reliability of resting‐state (RS) measurements has often been studied at standard field strengths, they have rarely been investigated using ultrahigh‐field (UHF) magnetic resonance imaging (MRI) systems. We investigated the intersession stability of four functional MRI RS parameters—amplitude of low‐frequency fluctuations (ALFF) and fractional ALFF (fALFF; representing the spontaneous brain activity), regional homogeneity (ReHo; measure of local connectivity), and degree centrality (DC; measure of long‐range connectivity)—in three RS networks, previously shown to play an important role in several psychiatric diseases—the default mode network (DMN), the central executive network (CEN), and the salience network (SN). Our investigation at individual subject space revealed a strong stability for ALFF, ReHo, and DC in all three networks, and a moderate level of stability in fALFF. Furthermore, the internetwork connectivity between each network pair was strongly stable between CEN/SN and moderately stable between DMN/SN and DMN/SN. The high degree of reliability and reproducibility in capturing the properties of the three major RS networks by means of UHF‐MRI points to its applicability as a potentially useful tool in the search for disease‐relevant biomarkers.

A Quantitative Data-Driven Analysis Framework for Resting-State Functional Magnetic Resonance Imaging: A Study of the Impact of Adult Age

The objective of this study is to introduce a new quantitative data-driven analysis (QDA) framework for the analysis of resting-state fMRI (R-fMRI) and use it to investigate the effect of adult age on resting-state functional connectivity (RFC). Whole-brain R-fMRI measurements were conducted on a 3T clinical MRI scanner in 227 healthy adult volunteers (N = 227, aged 18-76 years old, male/female = 99/128). With the proposed QDA framework we derived two types of voxel-wise RFC metrics: the connectivity strength index and connectivity density index utilizing the convolutions of the cross-correlation histogram with different kernels. Furthermore, we assessed the negative and positive portions of these metrics separately. With the QDA framework we found age-related declines of RFC metrics in the superior and middle frontal gyri, posterior cingulate cortex (PCC), right insula and inferior parietal lobule of the default mode network (DMN), which resembles previously reported results using other types of RFC data processing methods. Importantly, our new findings complement previously undocumented results in the following aspects: (1) the PCC and right insula are anti-correlated and tend to manifest simultaneously declines of both the negative and positive connectivity strength with subjects' age; (2) separate assessment of the negative and positive RFC metrics provides enhanced sensitivity to the aging effect; and (3) the sensorimotor network depicts enhanced negative connectivity strength with the adult age. The proposed QDA framework can produce threshold-free and voxel-wise RFC metrics from R-fMRI data. The detected adult age effect is largely consistent with previously reported studies using different R-fMRI analysis approaches. Moreover, the separate assessment of the negative and positive contributions to the RFC metrics can enhance the RFC sensitivity and clarify some of the mixed results in the literature regarding to the DMN and sensorimotor network involvement in adult aging.

An analytical workflow for seed-based correlation and independent component analysis in interventional resting-state fMRI studies

Resting-state functional MRI (rs-fMRI) is a task-free method of detecting spatially distinct brain regions with correlated activity, which form organised networks known as resting-state networks (RSNs). The two most widely used methods for analysing RSN connectivity are seed-based correlation analysis (SCA) and independent component analysis (ICA) but there is no established workflow of the optimal combination of analytical steps and how to execute them. Rodent rs-fMRI data from our previous longitudinal brain stimulation studies were used to investigate these two methods using FSL. Specifically, we examined: (1) RSN identification and group comparisons in ICA, (2) ICA-based denoising compared to nuisance signal regression in SCA, and (3) seed selection in SCA. In ICA, using a baseline-only template resulted in greater functional connectivity within RSNs and more sensitive detection of group differences than when an average pre/post stimulation template was used. In SCA, the use of an ICA-based denoising method in the preprocessing of rs-fMRI data and the use of seeds from individual functional connectivity maps in running group comparisons increased the sensitivity of detecting group differences by preventing the reduction in signals of interest. Accordingly, when analysing animal and human rs-fMRI data, we infer that the use of baseline-only templates in ICA and ICA-based denoising and individualised seeds in SCA will improve the reliability of results and comparability across rs-fMRI studies.

Network-wide abnormalities explain memory variability in hippocampal amnesia

Patients with hippocampal amnesia play a central role in memory neuroscience but the neural underpinnings of amnesia are hotly debated. We hypothesized that focal hippocampal damage is associated with changes across the extended hippocampal system and that these, rather than hippocampal atrophy per se, would explain variability in memory between patients. We assessed this hypothesis in a uniquely large cohort of patients (n = 38) after autoimmune limbic encephalitis, a syndrome associated with focal structural hippocampal pathology. These patients showed impaired recall, recognition and maintenance of new information, and remote autobiographical amnesia. Besides hippocampal atrophy, we observed correlatively reduced thalamic and entorhinal cortical volume, resting-state inter-hippocampal connectivity and activity in posteromedial cortex. Associations of hippocampal volume with recall, recognition, and remote memory were fully mediated by wider network abnormalities, and were only direct in forgetting. Network abnormalities may explain the variability across studies of amnesia and speak to debates in memory neuroscience.

Subjective memory complaints are associated with altered resting-state functional connectivity but not structural atrophy

Research indicates that a subtle cognitive decline, accompanied by pathological changes, occurs in individuals with subjective memory complaints (SMC). However, there is less evidence regarding the measurement of resting-state functional connectivity to detect subtle brain network alterations in neurodegenerative illnesses before cognitive change manifestation. We investigated the correlation between SMC and cognitive performance and explored functional and structural brain changes underlying SMC severity, using behavioral and brain imaging data-driven approaches. We observed that SMC was associated with depression but not with cognitive test scores, implying that SMC represent the “worried-well”; however, this model explains only 15% of the target variance. Using a conservative threshold, we observed connectivity related to SMC severity in the lingual gyrus, cuneus, anterior insula, and superior parietal lobule. Post-hoc analysis indicated that occipital and parietal functional connectivity increased with SMC severity. In contrast, volumetric alterations were not associated with SMC, even after applying a liberal threshold. Our findings suggest that altered resting-state functional connectivity in regions associated with SMC might reflect early compensatory changes that occur before cognitive and structural abnormalities develop.

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Subjective memory complaints are nearly independent from current cognition.

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Resting-state functional connectivity is related to subjective memory complaints.

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Brain structure has no association with subjective memory complaints.

Classifying early and late mild cognitive impairment stages of Alzheimer's disease by fusing default mode networks extracted with multiple seeds

BACKGROUND

The default mode network (DMN) in resting state has been increasingly used in disease diagnosis since it was found in 2001. Prior work has mainly focused on extracting a single DMN with various techniques. However, by using seeding-based analysis with more than one desirable seed, we can obtain multiple DMNs, which are likely to have complementary information, and thus are more promising for disease diagnosis. In the study, we used 18 early mild cognitive impairment (EMCI) participants and 18 late mild cognitive impairment (LMCI) participants of Alzheimer's disease (AD). First, we used seeding-based analysis with four seeds to extract four DMNs for each subject. Then, we conducted fusion analysis for all different combinations of the four DMNs. Finally, we carried out nonlinear support vector machine classification based on the mixing coefficients from the fusion analysis.

RESULTS

We found that (1) the four DMNs corresponding to the four different seeds indeed capture different functional regions of each subject; (2) Maps of the four DMNs in the most different joint source from fusion analysis are centered at the regions of the corresponding seeds; (3) Classification results reveal the effectiveness of using multiple seeds to extract DMNs. When using a single seed, the regions of posterior cingulate cortex (PCC) extractions of EMCI and LMCI show the largest difference. For multiple-seed cases, the regions of PCC extraction and right lateral parietal cortex (RLP) extraction provide complementary information for each other in fusion, which improves the classification accuracy. Furthermore, the regions of left lateral parietal cortex (LLP) extraction and RLP extraction also have complementary effect in fusion. In summary, AD diagnosis can be improved by exploiting complementary information of DMNs extracted with multiple seeds.

CONCLUSIONS

In this study, we applied fusion analysis to the DMNs extracted by using different seeds for exploiting the complementary information hidden among the separately extracted DMNs, and the results supported our expectation that using the complementary information can improve classification accuracy.

A Single Session of Robot-Controlled Proprioceptive Training Modulates Functional Connectivity of Sensory Motor Networks and Improves Reaching Accuracy in Chronic Stroke

BACKGROUND

Passive robot-generated arm movements in conjunction with proprioceptive decision making and feedback modulate functional connectivity (FC) in sensory motor networks and improve sensorimotor adaptation in normal individuals. This proof-of-principle study investigates whether these effects can be observed in stroke patients.

METHODS

A total of 10 chronic stroke patients with a range of stable motor and sensory deficits (Fugl-Meyer Arm score [FMA] 0-65, Nottingham Sensory Assessment [NSA] 10-40) underwent resting-state functional magnetic resonance imaging before and after a single session of robot-controlled proprioceptive training with feedback. Changes in FC were identified in each patient using independent component analysis as well as a seed region-based approach. FC changes were related to impairment and changes in task performance were assessed.

RESULTS

A single training session improved average arm reaching accuracy in 6 and proprioception in 8 patients. Two networks showing training-associated FC change were identified. Network C1 was present in all patients and network C2 only in patients with FM scores >7. Relatively larger C1 volume in the ipsilesional hemisphere was associated with less impairment ( r = 0.83 for NSA, r = 0.73 for FMA). This association was driven by specific regions in the contralesional hemisphere and their functional connections (supramarginal gyrus with FM scores r = 0.82, S1 with NSA scores r = 0.70, and cerebellum with NSA score r = -0.82).

CONCLUSION

A single session of robot-controlled proprioceptive training with feedback improved movement accuracy and induced FC changes in sensory motor networks of chronic stroke patients. FC changes are related to functional impairment and comprise bilateral sensory and motor network nodes.

Multilevel convergence of interoceptive impairments in hypertension: New evidence of disrupted body-brain interactions

Interoception, the sensing of visceral body signals, involves an interplay between neural and autonomic mechanisms. Clinical studies into this domain have focused on patients with neurological and psychiatric disorders, showing that damage to relevant brain mechanisms can variously alter interoceptive functions. However, the association between peripheral cardiac-system alterations and neurocognitive markers of interoception remains poorly understood. To bridge this gap, we examined multidimensional neural markers of interoception in patients with early stage of hypertensive disease (HTD) and healthy controls. Strategically, we recruited only HTD patients without cognitive impairment (as shown by neuropsychological tests), brain atrophy (as assessed with voxel-based morphometry), or white matter abnormalities (as evidenced by diffusion tensor imaging analysis). Interoceptive domains were assessed through (a) a behavioral heartbeat detection task; (b) measures of the heart-evoked potential (HEP), an electrophysiological cortical signature of attention to cardiac signals; and (c) neuroimaging recordings (MRI and fMRI) to evaluate anatomical and functional connectivity properties of key interoceptive regions (namely, the insula and the anterior cingulate cortex). Relative to controls, patients exhibited poorer interoceptive performance and reduced HEP modulations, alongside an abnormal association between interoceptive performance and both the volume and functional connectivity of the above regions. Such results suggest that peripheral cardiac-system impairments can be associated with abnormal behavioral and neurocognitive signatures of interoception. More generally, our findings indicate that interoceptive processes entail bidirectional influences between the cardiovascular and the central nervous systems.

Resting-state test-retest reliability of a priori defined canonical networks over different preprocessing steps

Resting-state functional connectivity analysis has become a widely used method for the investigation of human brain connectivity and pathology. The measurement of neuronal activity by functional MRI, however, is impeded by various nuisance signals that reduce the stability of functional connectivity. Several methods exist to address this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we here investigated the effect of various confound removal approaches on the test-retest reliability of functional-connectivity estimates in two previously defined functional brain networks. Our results showed that gray matter masking improved the reliability of connectivity estimates, whereas denoising based on principal components analysis reduced it. We additionally observed that refraining from using any correction for global signals provided the best test-retest reliability, but failed to reproduce anti-correlations between what have been previously described as antagonistic networks. This suggests that improved reliability can come at the expense of potentially poorer biological validity. Consistent with this, we observed that reliability was proportional to the retained variance, which presumably included structured noise, such as reliable nuisance signals (for instance, noise induced by cardiac processes). We conclude that compromises are necessary between maximizing test-retest reliability and removing variance that may be attributable to non-neuronal sources.