Resting-state Functional Connectivity Networks Research Articles

Predicting episodic and spatial memory performance from hippocampal resting-state functional connectivity: Evidence for an anterior-posterior division of function.

fMRI studies have identified distinct resting-state functional connectivity (RSFC) networks associated with the anterior and posterior hippocampus. However, the functional relevance of these two networks is still largely unknown. Hippocampal lesion studies and task-related fMRI point to a role for the anterior hippocampus in nonspatial episodic memory and the posterior hippocampus in spatial memory. We used Relevance Vector Regression (RVR), a machine-learning method that enables predictions of continuous outcome measures from multivariate patterns of brain imaging data, to test the hypothesis that patterns of whole-brain RSFC associated with the anterior hippocampus predict episodic memory performance, while patterns of whole-brain RSFC associated with the posterior hippocampus predict spatial memory performance. Magnetic resonance imaging and memory assessment took place at two separate occasions. The anterior and posterior RSFC largely corresponded with previous findings, and showed no effect of laterality. Supporting the hypothesis, RVR produced accurate predictions of episodic performance from anterior, but not posterior, RSFC, and accurate predictions of spatial performance from posterior, but not anterior, RSFC. In contrast, a univariate approach could not predict performance from resting-state connectivity. This supports a functional dissociation between the anterior and posterior hippocampus, and indicates a multivariate relationship between intrinsic functional networks and cognitive performance within specific domains, that is relatively stable over time.

The Default Mode Network as a Biomarker of Persistent Complaints after Mild Traumatic Brain Injury: A Longitudinal Functional Magnetic Resonance Imaging Study.

The objective of this study was to examine longitudinal functional connectivity of resting-state networks in patients with and without complaints after uncomplicated mild traumatic brain injury (mTBI). Second, we aimed to determine the value of network connectivity in predicting persistent complaints, anxiety, depression and long-term outcome. Thirty mTBI patients with three or more post-traumatic complaints at 2 weeks post-injury, 19 without complaints, and 20 matched healthy controls were selected for this study. Resting-state functional MRI (fMRI) was performed in patients at 1 month and 3 months post-injury, and once in healthy controls. Independent component analysis (ICA) was used to investigate the default mode, executive and salience networks. Persistent post-traumatic complaints, anxiety, and depression were measured at 3 months post-injury, and outcome was determined at 1 year post-injury. Within the group with complaints, higher functional connectivity between the anterior and posterior components of the default mode network at 1 month post-injury was associated with a greater number of complaints at 3 months post-injury (ρ = 0.59, p = 0.001). Minor longitudinal changes in functional connectivity were found for patients with and without complaints after mTBI, which were limited to connectivity within the precuneus component of the default mode network. No significant results were found for the executive and salience networks. Current results suggest that the default mode network may serve as a biomarker of persistent complaints in patients with uncomplicated mTBI.

Effects of neonatal deafness on resting-state functional network connectivity

Normal brain development depends on early sensory experience. Behavioral consequences of brain maturation in the absence of sensory input early in life are well documented. For example, experiments with mature, neonatally deaf human or animal subjects have revealed improved peripheral visual motion detection and spatial localization abilities. Such supranormal behavioral abilities in the nondeprived sensory modality are evidence of compensatory plasticity occurring in deprived brain regions at some point or throughout development. Sensory deprived brain regions may simply become unused neural real-estate resulting in a loss of function. Compensatory plasticity and loss of function are likely reflected in the differences in correlations between brain networks in deaf compared with hearing subjects. To address this, we used resting-state functional magnetic resonance imaging (fMRI) in lightly anesthetized hearing and neonatally deafened cats. Group independent component analysis (ICA) was used to identify 20 spatially distinct brain networks across all animals including auditory, visual, somatosensory, cingulate, insular, cerebellar, and subcortical networks. The resulting group ICA components were back-reconstructed to individual animal brains. The maximum correlations between the time-courses associated with each spatial component were computed using functional network connectivity (FNC). While no significant differences in the delay to peak correlations were identified between hearing and deaf cats, we observed 10 (of 190) significant differences in the amplitudes of between-network correlations. Six of the significant differences involved auditory-related networks and four involved visual, cingulate, or somatosensory networks. The results are discussed in context of known behavioral, electrophysiological, and anatomical differences following neonatal deafness. Furthermore, these results identify novel targets for future investigations at the neuronal level.

Anti-Fragmentation of Resting-State Functional Magnetic Resonance Imaging Connectivity Networks with Node-Wise Thresholding.

Functional magnetic resonance imaging (fMRI)-based functional connectivity networks are often constructed by thresholding a correlation matrix of nodal time courses. In a typical thresholding approach known as hard thresholding, a single threshold is applied to the entire correlation matrix to identify edges representing superthreshold correlations. However, hard thresholding is known to produce a network with uneven allocation of edges, resulting in a fragmented network with a large number of disconnected nodes. It is suggested that an alternative network thresholding approach, node-wise thresholding, is able to overcome these problems. To examine this, various network characteristics were compared between networks constructed by hard thresholding and node-wise thresholding, with publicly available resting-state fMRI data from 123 healthy young subjects. It was found that networks constructed with hard thresholding included a large number of disconnected nodes, while such network fragmentation was not observed in networks formed with node-wise thresholding. Moreover, in hard thresholding networks, fragmentized modular organization was observed, characterized by a large number of small modules. On the contrary, such modular fragmentation was not observed in node-wise thresholding networks, producing modules that were robust at any threshold and highly consistent across subjects. These results indicate that node-wise thresholding may lead to less fragmented networks. Moreover, node-wise thresholding enables robust characterization of network properties without much influence by the selection of a threshold.

Research progress about different levels of cognitive recession using resting state functional connectivity network methods

Normal brain aging and a serious of neurodegenerative diseases may lead to decline in memory, attention and executive ability and poorer quality of life. The mechanism of the decline is not clear now and is still a hot issue in the fields of neuroscience and medicine. A large number of researches showed that resting state functional brain networks based functional magnetic resonance imaging (fMRI) are sensitive and susceptive to the change of cognitive function. In this paper, the researches of brain functional connectivity based on resting fMRI in recent years were compared, and the results of subjects with different levels of cognitive decline including normal brain aging, mild cognitive impairment (MCI) and Alzheimer's disease (AD) were reviewed. And the changes of brain functional networks under three different levels of cognitive decline are introduced in this paper, which will provide the basis for the detection of normal brain aging and clinical diseases.

Hormonal Cycle and Contraceptive Effects on Amygdala and Salience Resting-State Networks in Women with Previous Affective Side Effects on the Pill.

The mechanisms linking ovarian hormones to negative affect are poorly characterized, but important clues may come from the examination of the brain's intrinsic organization. Here, we studied the effects of both the menstrual cycle and oral contraceptives (OCs) on amygdala and salience network resting-state functional connectivity using a double-blind, randomized, and placebo-controlled design. Hormone levels, depressive symptoms, and resting-state functional connectivity were measured in 35 healthy women (24.9±4.2 years) who had previously experienced OC-related negative affect. All participants were examined in the follicular phase of a baseline cycle and in the third week of the subsequent cycle during treatment with either a combined OC (30 μg ethinyl estradiol/0.15 mg levonorgestrel) or placebo. The latter time point targeted the midluteal phase in placebo users and steady-state ethinyl estradiol and levonorgestrel concentrations in OC users. Amygdala and salience network connectivity generally increased with both higher endogenous and synthetic hormone levels, although amygdala-parietal cortical connectivity decreased in OC users. When in the luteal phase, the naturally cycling placebo users demonstrated higher connectivity in both networks compared with the women receiving OCs. Our results support a causal link between the exogenous administration of synthetic hormones and amygdala and salience network connectivity. Furthermore, they suggest a similar, potentially stronger, association between the natural hormonal variations across the menstrual cycle and intrinsic network connectivity.

Effect of Resting-State fNIRS Scanning Duration on Functional Brain Connectivity and Graph Theory Metrics of Brain Network.

As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.

Resisting Sleep Pressure: Impact on Resting State Functional Network Connectivity.

In today's 24/7 society, sleep restriction is a common phenomenon which leads to increased levels of sleep pressure in daily life. However, the magnitude and extent of impairment of brain functioning due to increased sleep pressure is still not completely understood. Resting state network (RSN) analyses have become increasingly popular because they allow us to investigate brain activity patterns in the absence of a specific task and to identify changes under different levels of vigilance (e.g. due to increased sleep pressure). RSNs are commonly derived from BOLD fMRI signals but studies progressively also employ cerebral blood flow (CBF) signals. To investigate the impact of sleep pressure on RSNs, we examined RSNs of participants under high (19h awake) and normal (10h awake) sleep pressure with three imaging modalities (arterial spin labeling, BOLD, pseudo BOLD) while providing confirmation of vigilance states in most conditions. We demonstrated that CBF and pseudo BOLD signals (measured with arterial spin labeling) are suited to derive independent component analysis based RSNs. The spatial map differences of these RSNs were rather small, suggesting a strong biological substrate underlying these networks. Interestingly, increased sleep pressure, namely longer time awake, specifically changed the functional network connectivity (FNC) between RSNs. In summary, all FNCs of the default mode network with any other network or component showed increasing effects as a function of increased 'time awake'. All other FNCs became more anti-correlated with increased 'time awake'. The sensorimotor networks were the only ones who showed a within network change of FNC, namely decreased connectivity as function of 'time awake'. These specific changes of FNC could reflect both compensatory mechanisms aiming to fight sleep as well as a first reduction of consciousness while becoming drowsy. We think that the specific changes observed in functional network connectivity could imply an impairment of information transfer between the affected RSNs.

Mindfulness Meditation Training and Executive Control Network Resting State Functional Connectivity: A Randomized Controlled Trial.

Mindfulness meditation training has been previously shown to enhance behavioral measures of executive control (e.g., attention, working memory, cognitive control), but the neural mechanisms underlying these improvements are largely unknown. Here, we test whether mindfulness training interventions foster executive control by strengthening functional connections between dorsolateral prefrontal cortex (dlPFC)-a hub of the executive control network-and frontoparietal regions that coordinate executive function. Thirty-five adults with elevated levels of psychological distress participated in a 3-day randomized controlled trial of intensive mindfulness meditation or relaxation training. Participants completed a resting state functional magnetic resonance imaging scan before and after the intervention. We tested whether mindfulness meditation training increased resting state functional connectivity (rsFC) between dlPFC and frontoparietal control network regions. Left dlPFC showed increased connectivity to the right inferior frontal gyrus (T = 3.74), right middle frontal gyrus (MFG) (T = 3.98), right supplementary eye field (T = 4.29), right parietal cortex (T = 4.44), and left middle temporal gyrus (T = 3.97, all p < .05) after mindfulness training relative to the relaxation control. Right dlPFC showed increased connectivity to right MFG (T = 4.97, p < .05). We report that mindfulness training increases rsFC between dlPFC and dorsal network (superior parietal lobule, supplementary eye field, MFG) and ventral network (right IFG, middle temporal/angular gyrus) regions. These findings extend previous work showing increased functional connectivity among brain regions associated with executive function during active meditation by identifying specific neural circuits in which rsFC is enhanced by a mindfulness intervention in individuals with high levels of psychological distress. Clinicaltrials.gov,NCT01628809.

Decreased hemispheric connectivity and decreased intra- and inter- hemisphere asymmetry of resting state functional network connectivity in schizophrenia.

Many studies have shown that schizophrenia patients have aberrant functional network connectivity (FNC) among brain regions, suggesting schizophrenia manifests with significantly diminished (in majority of the cases) connectivity. Schizophrenia is also associated with a lack of hemispheric lateralization. Hoptman et al. (2012) reported lower inter-hemispheric connectivity in schizophrenia patients compared to controls using voxel-mirrored homotopic connectivity. In this study, we merge these two points of views together using a group independent component analysis (gICA)-based approach to generate hemisphere-specific timecourses and calculate intra-hemisphere and inter-hemisphere FNC on a resting state fMRI dataset consisting of age- and gender-balanced 151 schizophrenia patients and 163 healthy controls. We analyzed the group differences between patients and healthy controls in each type of FNC measures along with age and gender effects. The results reveal that FNC in schizophrenia patients shows less hemispheric asymmetry compared to that of the healthy controls. We also found a decrease in connectivity in all FNC types such as intra-left (L_FNC), intra-right (R_FNC) and inter-hemisphere (Inter_FNC) in the schizophrenia patients relative to healthy controls, but general patterns of connectivity were preserved in patients. Analyses of age and gender effects yielded results similar to those reported in whole brain FNC studies.

Resting-state functional network connectivity differs between healthy aged individuals with and without reports of subjective cognitive change (P6.096)

Resting-state functional network connectivity differs between healthy aged individuals with and without reports of subjective cognitive change (P6.096)

Functional Connectivity is Reduced in Early-stage Primary Progressive Aphasia When Atrophy is not Prominent.

Primary progressive aphasia (PPA) is a clinical syndrome of language decline caused by neurodegenerative pathology. Although language impairments in PPA are typically localized via the morphometric assessment of atrophy, functional changes may accompany or even precede detectable structural alterations, in which case resting state functional connectivity (RSFC) could provide an alternative approach. The goal of this study was to determine whether language network RSFC is reduced in early-stage PPA when atrophy is not prominent. We identified 10 individuals with early-stage agrammatic variant of PPA with no prominent cortical thinning compared with nonaphasic controls. RSFC between 2 nodes of the language network and 2 nodes of the default mode network were compared between agrammatic variant of PPA and healthy control participants. Language network connectivity was comparable with controls among patients with milder agrammatism, but was significantly reduced in patients with more pronounced agrammatism. No group differences were observed in default mode network connectivity, demonstrating specificity of findings. In early stages of PPA when cortical atrophy is not prominent, RSFC provides an alternative method for probing the neuroanatomic substrates of language impairment. RSFC may be of particular utility in studies on early interventions for neurodegenerative disease, either to identify anatomic targets for intervention or as an outcome measure of therapeutic efficacy.

Reduced Resting-State Functional Connectivity in Current and Recovered Restrictive Anorexia Nervosa.

Functional connectivity studies based on resting-state functional magnetic resonance imaging (rs-fMRI) have shown alterations in brain networks associated with self-referential processing, cognitive control, and somatosensory processing in anorexia nervosa (AN). This study aimed to further investigate the functional connectivity of resting-state networks (RSNs) in homogenous subsamples of individuals with restrictive AN (current and recovered) and the relationship this has with core eating disorder psychopathology. rs-fMRI scans were obtained from 12 female individuals with restrictive AN, 14 females recovered from restrictive AN, and 16 female healthy controls. Independent components analysis revealed a set of functionally relevant RSNs, previously reported in the literature. Dual regression analysis showed decreased temporal coherence within the lateral visual and auditory RSNs in individuals with current AN and those recovered from AN compared to healthy individuals. This decreased connectivity was also found in regions associated with somatosensory processing, and is consistent with reduced interoceptive awareness and body image perception, characteristic of AN. Widespread gray matter (GM) reductions were also found in both the AN groups, and differences in functional connectivity were no longer significant when GM maps were added as a covariate in the dual regression analysis. This raises the possibility that deficits in somatosensory and interoceptive processing observed in AN may be in part underpinned or exacerbated by GM reductions.

Functional connectivity in amygdalar-sensory/(pre)motor networks at rest: new evidence from the Human Connectome Project.

The word 'e-motion' derives from the Latin word 'ex-moveo' which literally means 'moving away from something/somebody'. Emotions are thus fundamental to prime action and goal-directed behavior with obvious implications for individual's survival. However, the brain mechanisms underlying the interactions between emotional and motor cortical systems remain poorly understood. A recent diffusion tensor imaging study in humans has reported the existence of direct anatomical connections between the amygdala and sensory/(pre)motor cortices, corroborating an initial observation in animal research. Nevertheless, the functional significance of these amygdala-sensory/(pre)motor pathways remain uncertain. More specifically, it is currently unclear whether a distinct amygdala-sensory/(pre)motor circuit can be identified with resting-state functional magnetic resonance imaging (rs-fMRI). This is a key issue, as rs-fMRI offers an opportunity to simultaneously examine distinct neural circuits that underpin different cognitive, emotional and motor functions, while minimizing task-related performance confounds. We therefore tested the hypothesis that the amygdala and sensory/(pre)motor cortices could be identified as part of the same resting-state functional connectivity network. To this end, we examined independent component analysis results in a very large rs-fMRI data-set drawn from the Human Connectome Project (n=820 participants, mean age: 28.5years). To our knowledge, we report for the first time the existence of a distinct amygdala-sensory/(pre)motor functional network at rest. rs-fMRI studies are now warranted to examine potential abnormalities in this circuit in psychiatric and neurological diseases that may be associated with alterations in the amygdala-sensory/(pre)motor pathways (e.g. conversion disorders, impulse control disorders, amyotrophic lateral sclerosis and multiple sclerosis).

Sources of disconnection in neurocognitive aging: cerebral white-matter integrity, resting-state functional connectivity, and white-matter hyperintensity volume

Age-related decline in fluid cognition can be characterized as a disconnection among specific brain structures, leading to a decline in functional efficiency. The potential sources of disconnection, however, are unclear. We investigated imaging measures of cerebral white-matter integrity, resting-state functional connectivity, and white-matter hyperintensity volume as mediators of the relation between age and fluid cognition, in 145 healthy, community-dwelling adults 19–79 years of age. At a general level of analysis, with a single composite measure of fluid cognition and single measures of each of the 3 imaging modalities, age exhibited an independent influence on the cognitive and imaging measures, and the imaging variables did not mediate the age-cognition relation. At a more specific level of analysis, resting-state functional connectivity of sensorimotor networks was a significant mediator of the age-related decline in executive function. These findings suggest that different levels of analysis lead to different models of neurocognitive disconnection, and that resting-state functional connectivity, in particular, may contribute to age-related decline in executive function.

Does Conservative Treatment Change the Brain in Patients with Chronic Musculoskeletal Pain? A Systematic Review

Background: Chronic musculoskeletal pain is characterized by maladaptive central neuroplastic changes. Many observational studies have demonstrated that chronic pain states are associated with brain alterations regarding structure and/or function. Rehabilitation of patients with chronic musculoskeletal pain may include cognitive, exercise, or multimodal therapies. Objective: The current review aims to provide a constructive overview of the existing literature reporting neural correlates, based on brain magnetic resonance imaging (MRI) techniques, following conservative treatment in chronic musculoskeletal pain patients. Study Design: Systematic review of the literature. Methods: The current review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Literature was searched from 3 databases and screened for eligibility. Methodological quality across studies was assessed with Cochrane Collaboration’s tool for assessing risk of bias and quality of evidence was determined applying the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) approach. Results: A total of 9 eligible studies were identified with a predominant high risk of bias. Cognitive behavioral therapy induced several structural and functional changes predominantly in prefrontal cortical regions and a shift from affective to sensory-discriminative brain activity after behavioral extinction training. Multidisciplinary treatment in pediatric complex regional pain syndrome facilitated normalization of functional connectivity of resting-state networks and the amygdala, and increased gray matter in prefrontal and specific subcortical areas. Exercise therapy led to specific for resting-state functional connectivity and a trend towards pressure-induced brain activity changes. Limitations: A very small number of studies was available, which furthermore exhibited small study samples. Moreover, only 2 of the included studies were randomized controlled trials. Conclusions: It is likely that conservative treatments may induce mainly functional and structural brain changes in prefrontal regions in patients with chronic musculoskeletal pain. Due to the relatively high risk of bias across the included studies, future studies with randomized designs are needed to confirm the current findings. In addition, more research evaluating the treatmentinduced effects on white matter and whole-brain network dynamics are warranted. Key words: Chronic pain, musculoskeletal pain, MRI, functional MRI, therapy, rehabilitation, cognitive behavioral therapy, exercise therapy

The left middle temporal gyrus in the middle of an impaired social-affective communication network in social anxiety disorder

BackgroundPrevious studies on patients diagnosed with social anxiety disorder (SAD) reported changed patterns of the resting-state functional connectivity network (rs-FCN) between the prefrontal cortices and other prefrontal, amygdalar or striatal regions. Using a graph theory approach, this study explored the modularity-based community profile and patterns of inter-/intra-modular communication for the rs-FCN in SAD. MethodsIn total, for 28 SAD patients and 27 healthy controls (HC), functional magnetic resonance imaging (fMRI) data were acquired in resting-state and subjected to a graph theory analysis. ResultsThe within-module degree z-score for a hub region [out of a total of 10 hub regions ranked using the participation coefficient] named left middle temporal gyrus was impaired in SAD compared to HC, proportional to the severity of clinician-scored and patient-reported functional impairment in SAD. LimitationsMost of participants included in this study were undergraduate students in their early-to-mid 20's. ConclusionsThis study showed the importance of functional communication from the left middle temporal gyrus with other opercular-insular-subcortical regions for better objective functioning and lesser subjective disability in SAD.

Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations

Graph theoretical analysis has become an important tool in the examination of brain dysconnectivity in neurological and psychiatric brain disorders. A common analysis step in the construction of the functional graph or network involves “thresholding” of the connectivity matrix, selecting the set of edges that together form the graph on which network organization is evaluated. To avoid systematic differences in absolute number of edges, studies have argued against the use of an “absolute threshold” in case-control studies and have proposed the use of “proportional thresholding” instead, in which a pre-defined number of strongest connections are selected as network edges, ensuring equal network density across datasets. Here, we systematically studied the effect of proportional thresholding on the construction of functional matrices and subsequent graph analysis in patient-control functional connectome studies. In a few simple experiments we show that differences in overall strength of functional connectivity (FC) – as often observed between patients and controls – can have predictable consequences for between-group differences in network organization. In individual networks with lower overall FC the proportional thresholding algorithm has to select more edges based on lower correlations, which have (on average) a higher probability of being spurious, and thus introduces a higher degree of randomness in the resulting network. We show across both empirical and artificial patient-control datasets that lower levels of overall FC in either the patient or control group will most often lead to differences in network efficiency and clustering, suggesting that differences in FC across subjects will be artificially inflated or translated into differences in network organization. Based on the presented case-control findings we inform about the caveats of proportional thresholding in patient-control studies in which groups show a between-group difference in overall FC. We make recommendations on how to examine, report and to take into account overall FC effects in future patient-control functional connectome studies.

Influence of age and cognitive performance on resting-state brain networks of older adults in a population-based cohort

Aging leads to global changes in brain structure and cognitive performance, with reorganization of functional brain networks. Importantly, these age-related changes show higher inter-individual variability in older subjects. To particularly address this variability is a challenge for studies on lifetime trajectories from early to late adulthood. The present study therefore had a dedicated focus on late adulthood to characterize the functional connectivity in resting-state networks (RSFC) in relation to age and cognitive performance in 711 older adults (55–85 years) from the 1000BRAINS project. The executive, left and right frontoparietal resting-state (RS) networks showed age-related increases in RSFC. However, older adults did not show changes in RSFC in the default mode network (DMN). Furthermore, lower performance in working memory (WM) was associated with higher RSFC in the left frontoparietal RS network. The results suggest age-related compensatory increases in RSFC which might help to maintain cognitive performance. Nevertheless, the negative correlation between RSFC and WM performance hints at limited cognitive reserve capacity in lower performing older adults. Consequently, the current results provide evidence for a functional reorganization of the brain until late adulthood that might additionally explain parts of the variability of cognitive abilities in older adults.

Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression.

The large number of multicollinear regional features that are provided by resting state (rs) fMRI data requires robust feature selection to uncover consistent networks of functional disconnection in Alzheimer's disease (AD). Here, we compared elastic net regularized and classical stepwise logistic regression in respect to consistency of feature selection and diagnostic accuracy using rs-fMRI data from four centers of the “German resting-state initiative for diagnostic biomarkers” (psymri.org), comprising 53 AD patients and 118 age and sex matched healthy controls. Using all possible pairs of correlations between the time series of rs-fMRI signal from 84 functionally defined brain regions as the initial set of predictor variables, we calculated accuracy of group discrimination and consistency of feature selection with bootstrap cross-validation. Mean areas under the receiver operating characteristic curves as measure of diagnostic accuracy were 0.70 in unregularized and 0.80 in regularized regression. Elastic net regression was insensitive to scanner effects and recovered a consistent network of functional connectivity decline in AD that encompassed parts of the dorsal default mode as well as brain regions involved in attention, executive control, and language processing. Stepwise logistic regression found no consistent network of AD related functional connectivity decline. Regularized regression has high potential to increase diagnostic accuracy and consistency of feature selection from multicollinear functional neuroimaging data in AD. Our findings suggest an extended network of functional alterations in AD, but the diagnostic accuracy of rs-fMRI in this multicenter setting did not reach the benchmark defined for a useful biomarker of AD.