Resting-state BOLD Signal Research Articles

White matter engagement in brain networks assessed by integration of functional and structural connectivity

Current models of brain networks may potentially be improved by integrating our knowledge of structural connections, within and between circuits, with metrics of functional interactions between network nodes. The former may be obtained from diffusion MRI of white matter (WM), while the latter may be derived by measuring correlations between resting state BOLD signals from pairs of gray matter (GM) regions. From inspection of diffusion MRI data, it is clear that each WM voxel within a 3D image array may be traversed by multiple WM structural tracts, each of which connects a pair of GM nodes. We hypothesized that by appropriately weighting and then integrating the functional connectivity of each such connected pair, the overall engagement of any WM voxel in brain functions could be evaluated. This model introduces a structural constraint to earlier studies of WM engagement and addresses some limitations of previous efforts to relate structure and function. Using concepts derived from graph theory, we obtained spatial maps of WM engagement which highlight WM regions critical for efficient communications across the brain. The distributions of WM engagement are highly reproducible across subjects and depict a notable interdependence between the distribution of GM activities and the detailed organization of WM. Additionally, we provide evidence that the engagement varies over time and shows significant differences between genders. These findings suggest the potential of WM engagement as a measure of the integrity of normal brain functions and as a biomarker for neurological and cognitive disorders.

374. Altered Cerebellar Ventral Tegmental Area Functional Connectivity With Decreased Social Motivation in Schizophrenia Using Resting State Bold Signal Variability - Preliminary Results

374. Altered Cerebellar Ventral Tegmental Area Functional Connectivity With Decreased Social Motivation in Schizophrenia Using Resting State Bold Signal Variability - Preliminary Results

Reorganization of resting-state brain network functional connectivity across human brain developmental stages

Cognitive brain aging can either be healthy or degenerative in nature. Multiple alterations occur in brain networks with healthy aging. Much of this has yet to be investigated. This study seeks to understand the typical healthy human brain’s developmental stages using a publicly available dataset from the human connectome project. As the human brain’s developmental stage varies, we also intend to identify a pattern of reorganization in the resting state functional connectivity of several brain networks. The results are specifically presented based on the resting state BOLD signals of 1096 healthy volunteers between the age group of 7–89 years. The k-means clustering method has been used to determine the various human brain developmental stages. Using the t-SNE technique, the clusters are visually separated. BrainNet Viewer is used to study the changes in resting state functional connectivity of the entire brain as the human brain developmental stages vary. The age-related pattern of change in the resting state functional connectivity of six Dosenbasch brain networks that were grouped using the k-means elbow approach has been additionally presented. For performance evaluation, three metrics of brain network connection including network segregation, between network connectivity, and within-network connectivity are used. As the age cohort changes, a consistent pattern in the variance of these connection indices is seen for different Dosenbasch brain networks. Thus, the study’s findings suggest that healthy aging causes a functional reorganization of the resting state brain network connections.

Resting-state BOLD signal variability is associated with individual differences in metacontrol

Numerous studies demonstrate that moment-to-moment neural variability is behaviorally relevant and beneficial for tasks and behaviors requiring cognitive flexibility. However, it remains unclear whether the positive effect of neural variability also holds for cognitive persistence. Moreover, different brain variability measures have been used in previous studies, yet comparisons between them are lacking. In the current study, we examined the association between resting-state BOLD signal variability and two metacontrol policies (i.e., persistence vs. flexibility). Brain variability was estimated from resting-state fMRI (rsfMRI) data using two different approaches (i.e., Standard Deviation (SD), and Mean Square Successive Difference (MSSD)) and metacontrol biases were assessed by three metacontrol-sensitive tasks. Results showed that brain variability measured by SD and MSSD was highly positively related. Critically, higher variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and higher variability measured by SD in the parietal and motor network, parietal and frontal network were associated with reduced persistence (or greater flexibility) of metacontrol (i.e., larger Stroop effect or worse RAT performance). These results show that the beneficial effect of brain signal variability on cognitive control depends on the metacontrol states involved. Our study highlights the importance of temporal variability of rsfMRI activity in understanding the neural underpinnings of cognitive control.

Effects of single- and multiple-dose oxytocin treatment on amygdala low-frequency BOLD fluctuations and BOLD spectral dynamics in autism

Prior neuroimaging clinical trials investigating the neural effects of intranasal administration of the neuropeptide oxytocin demonstrated a key role of the amygdala in oxytocin’s neuromodulatory effects. These studies mostly demonstrated the acute effects of single-dose administrations, examining task-dependent effects of oxytocin on brain activity elicited during explicit experimental tasks or stimuli presentations. The increased consideration of oxytocin as a potential ameliorating treatment in autism spectrum disorder (ASD) requires a better understanding of how multiple-dose oxytocin administration affects intrinsic, task-free, amygdala function. In this double-blind, randomized, placebo-controlled trial with between-subject design, 38 adult men with ASD underwent resting-state fMRI scanning before and after oxytocin or placebo treatment. Effects were assessed either after a single-dose administration, consisting of 24 international units, or after multiple-dose treatment, consisting of 4 weeks of once-daily nasal spray administrations. Compared to placebo, oxytocin induced a decrease in intrinsic resting-state BOLD signal amplitudes of the bilateral amygdala (fractional amplitudes of low-frequency fluctuations) and modulated cross-frequency interactions between adjacent BOLD frequency components. The right amygdala showed a pattern of reduced cross-frequency harmonicity, while the left amygdala showed a relative increase in harmonic cross-frequency interactions after oxytocin treatment. Notably, the direction and magnitude of BOLD spectral changes induced after a single-dose were qualitatively similar to treatment effects induced after multiple-dose treatment. Furthermore, the identified spectral changes in amygdalar BOLD amplitude and cross-frequency harmonicity were associated with improved feelings of tension, reflecting oxytocin’s anxiolytic, stress-reducing neuromodulatory role. The observed effects of oxytocin on amygdalar BOLD spectral characteristics and associated behaviors contribute to a deeper mechanistic understanding of the intrinsic, task-free neuromodulatory dynamics that underlie single- and multiple-dose oxytocin treatment in ASD. European Clinical Trial Registry (Eudract 2014-000586-45).

What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics.

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, most efforts of capturing the causal mechanistic generating principles have supposed underlying stationarity, being unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing a topological structure associated to the brain state at each moment in time (its attractor or ‘information structure’), we are able to classify different brain states by using the statistics across time of these structures hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify resting-state BOLD fMRI signals from two classes of post-comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

Covariance-based vs. correlation-based functional connectivity dissociates healthy aging from Alzheimer disease

Prior studies of aging and Alzheimer disease have evaluated resting state functional connectivity (FC) using either seed-based correlation (SBC) or independent component analysis (ICA), with a focus on particular functional systems. SBC and ICA both are insensitive to differences in signal amplitude. At the same time, accumulating evidence indicates that the amplitude of spontaneous BOLD signal fluctuations is physiologically meaningful. We systematically compared covariance-based FC, which is sensitive to amplitude, vs. correlation-based FC, which is not, in affected individuals and controls drawn from two cohorts of participants including autosomal dominant Alzheimer disease (ADAD), late onset Alzheimer disease (LOAD), and age-matched controls. Functional connectivity was computed over 222 regions of interest and group differences were evaluated in terms of components projected onto a space of lower dimension. Our principal observations are: (1) Aging is associated with global loss of resting state fMRI signal amplitude that is approximately uniform across resting state networks. (2) Thus, covariance FC measures decrease with age whereas correlation FC is relatively preserved in healthy aging. (3) In contrast, symptomatic ADAD and LOAD both lead to loss of spontaneous activity amplitude as well as severely degraded correlation structure. These results demonstrate a double dissociation between age vs. Alzheimer disease and the amplitude vs. correlation structure of resting state BOLD signals. Modeling results suggest that the AD-associated loss of correlation structure is attributable to a relative increase in the fraction of locally restricted as opposed to widely shared variance.

Modeling the Hemodynamic Response Function Using EEG-fMRI Data During Eyes-Open Resting-State Conditions and Motor Task Execution

Being able to accurately quantify the hemodynamic response function (HRF) that links the blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) signal to the underlying neural activity is important both for elucidating neurovascular coupling mechanisms and improving the accuracy of fMRI-based functional connectivity analyses. In particular, HRF estimation using BOLD-fMRI is challenging particularly in the case of resting-state data, due to the absence of information about the underlying neuronal dynamics. To this end, using simultaneously recorded electroencephalography (EEG) and fMRI data is a promising approach, as EEG provides a more direct measure of neural activations. In the present work, we employ simultaneous EEG-fMRI to investigate the regional characteristics of the HRF using measurements acquired during resting conditions. We propose a novel methodological approach based on combining distributed EEG source space reconstruction, which improves the spatial resolution of HRF estimation and using block-structured linear and nonlinear models, which enables us to simultaneously obtain HRF estimates and the contribution of different EEG frequency bands. Our results suggest that the dynamics of the resting-state BOLD signal can be sufficiently described using linear models and that the contribution of each band is region specific. Specifically, it was found that sensory-motor cortices exhibit positive HRF shapes, whereas the lateral occipital cortex and areas in the parietal cortex, such as the inferior and superior parietal lobule exhibit negative HRF shapes. To validate the proposed method, we repeated the analysis using simultaneous EEG-fMRI measurements acquired during execution of a unimanual hand-grip task. Our results reveal significant associations between BOLD signal variations and electrophysiological power fluctuations in the ipsilateral primary motor cortex, particularly for the EEG beta band, in agreement with previous studies in the literature.

RsHRF: A toolbox for resting-state HRF estimation and deconvolution

The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.

Detection of functional networks within white matter using independent component analysis

Spontaneous fluctuations in MRI signals from gray matter (GM) in the brain are interpreted as originating from variations in neural activity, and their inter-regional correlations may be analyzed to reveal functional connectivity. However, most studies of intrinsic neuronal activity have ignored the spontaneous fluctuations that also arise in white matter (WM). In this work, we explore spontaneous fluctuations in resting state MRI signals in WM based on spatial independent component analyses (ICA), a data-driven approach that separates signals into independent sources without making specific modeling assumptions. ICA has become widely accepted as a valuable approach for identifying functional connectivity within cortex but has been rarely applied to derive equivalent structures within WM. Here, BOLD signal changes in WM of a group of subjects performing motor tasks were first detected using ICA, and a spatial component whose time course was consistent with the task was found, demonstrating the analysis is sensitive to evoked BOLD signals in WM. Secondly, multiple spatial components were derived by applying ICA to identify those voxels in WM whose MRI signals showed similar temporal behaviors in a resting state. These functionally-related structures are grossly symmetric and coincide with corresponding tracts identified from diffusion MRI. Finally, functional connectivity was quantified by calculating correlations between pairs of structures to explore the synchronicity of resting state BOLD signals across WM regions, and the experimental results revealed that there exist two distinct groupings of functional correlations in WM tracts at rest. Our study provides further insights into the nature of activation patterns, functional responses and connectivity in WM, and support previous suggestions that BOLD signals in WM show similarities with cortical activations and are characterized by distinct underlying structures in tasks and at rest.

The association between resting-state functional magnetic resonance imaging and aortic pulse-wave velocity in healthy adults.

Resting‐state functional magnetic resonance imaging (rs‐fMRI) is frequently used to study brain function; but, it is unclear whether BOLD‐signal fluctuation amplitude and functional connectivity are associated with vascular factors, and how vascular‐health factors are reflected in rs‐fMRI metrics in the healthy population. As arterial stiffening is a known age‐related cardiovascular risk factor, we investigated the associations between aortic stiffening (as measured using pulse‐wave velocity [PWV]) and rs‐fMRI metrics. We used cardiac MRI to measure aortic PWV (an established indicator of whole‐body vascular stiffness), as well as dual‐echo pseudo‐continuous arterial‐spin labeling to measure BOLD and CBF dynamics simultaneously in a group of generally healthy adults. We found that: (1) higher aortic PWV is associated with lower variance in the resting‐state BOLD signal; (2) higher PWV is also associated with lower BOLD‐based resting‐state functional connectivity; (3) regions showing lower connectivity do not fully overlap with those showing lower BOLD variance with higher PWV; (4) CBF signal variance is a significant mediator of the above findings, only when averaged across regions‐of‐interest. Furthermore, we found no significant association between BOLD signal variance and systolic blood pressure, which is also a known predictor of vascular stiffness. Age‐related vascular stiffness, as measured by PWV, provides a unique scenario to demonstrate the extent of vascular bias in rs‐fMRI signal fluctuations and functional connectivity. These findings suggest that a substantial portion of age‐related rs‐fMRI differences may be driven by vascular effects rather than directly by brain function.

Scale-free properties of resting-state BOLD signal change after fear learning

Scale-free properties of resting-state BOLD signal change after fear learning

Sensory-motor network functional connectivity in children with unilateral cerebral palsy secondary to perinatal stroke.

BackgroundPerinatal stroke is the most common cause of unilateral cerebral palsy. Mechanisms of post-stroke developmental plasticity in children are poorly understood. To better understand the relationship between functional connectivity and disability, we used resting-state fMRI to compare sensorimotor connectivity with clinical dysfunction.MethodsSchool-aged children with periventricular venous infarction (PVI) and unilateral cerebral palsy were compared to controls. Resting-state BOLD signal was acquired on 3 T MRI and analyzed using CONN in SPM12. Functional connectivity was computed between S1, M1, supplementary motor area (SMA), and thalamus of the left/non-lesioned and right/lesioned hemisphere. Primary outcome was connectivity expressed as a Fisher-transformed correlation coefficient. Motor function was measured using the Assisting Hand Assessment (AHA), and Melbourne Assessment (MA). Proprioceptive function was measured using a robotic position matching task (VarXY).ResultsParticipants included 15 PVI and 21 controls. AHA and MA in stroke patients were negatively correlated with connectivity (increased connectivity = poorer performance). Position sense was inversely correlated with connectivity (increased connectivity = improved performance) between the non-lesioned S1 and thalamus/SMA. In controls, VarXY was positively correlated with connectivity between the thalamus and bilateral sensorimotor regions.ConclusionsResting state fMRI measures of sensorimotor connectivity are associated with clinical sensorimotor function in children with unilateral cerebral palsy secondary to PVI. Greater insight into understanding reorganization of brain networks following perinatal stroke may facilitate personalized rehabilitation.

Non-invasive evaluation of cerebral perfusion in patients with transient ischemic attack: an fMRI study.

Detection of hypoperfused tissue due to the ischemia is considered to be important in understanding the cerebral perfusion status and may be helpful in guiding therapeutic decisions for patients with transient ischemic attack (TIA). We hypothesized that the combination of two non-invasive fMRI techniques: resting-state BOLD-fMRI time-shift analysis (TSA) approach and 3D ASL, could detect the cerebral hemodynamic status in TIA patients noninvasively. From April 2015 to June 2016, 51 TIA patients were recruited in this study. We calculated the time delay between the resting-state BOLD signal at each voxel and the whole-brain signal using TSA approach and compared the results to CBF map derived from ASL. Out of the 51 patients, 24 patients with normal arrival time and CBF were in Stage 0; 14 patients who showed delayed arrival time and normal CBF which indicated elevated CBV were in Stage I; the other 13 patients who had both delayed arrival time and decreased CBF were in Stage II, the group average spatial overlap, i.e., Dice coefficient, of the two measurements was 0.55. Four patients in Stage 0 (17.4%), three patients in Stage I (23.1%) and five patients in Stage II (45.5%) suffered ischemic stroke or TIA symptoms in 1 year after MRI scan. The patients in Stage II was at highest risk of subsequent events when compared to other two stages. The combination of resting-state BOLD-fMRI and ASL hold the potential to noninvasively identify the hemodynamic status in TIA patients and help predict the risk of subsequent events.

Functional tractography of white matter by high angular resolution functional-correlation imaging (HARFI).

Functional magnetic resonance imaging with BOLD contrast is widely used for detecting brain activity in the cortex. Recently, several studies have described anisotropic correlations of resting-state BOLD signals between voxels in white matter (WM). These local WM correlations have been modeled as functional-correlation tensors, are largely consistent with underlying WM fiber orientations derived from diffusion MRI, and appear to change during functional activity. However, functional-correlation tensors have several limitations. The use of only nearest-neighbor voxels makes functional-correlation tensors sensitive to noise. Furthermore, adjacent voxels tend to have higher correlations than diagonal voxels, resulting in orientation-related biases. Finally, the tensor model restricts functional correlations to an ellipsoidal bipolar-symmetric shape, and precludes the ability to detect complex functional orientation distributions (FODs). We introduce high-angular-resolution functional-correlation imaging (HARFI) to address these limitations. In the same way that high-angular-resolution diffusion imaging (HARDI) techniques provide more information than diffusion tensors, we show that the HARFI model is capable of characterizing complex FODs expected to be present in WM. We demonstrate that the unique radial and angular sampling strategy eliminates orientation biases present in tensor models. We further show that HARFI FODs are able to reconstruct known WM pathways. Finally, we show that HARFI allows asymmetric "bending" and "fanning" distributions, and propose asymmetric and functional indices which may increase fiber tracking specificity, or highlight boundaries between functional regions. The results suggest the HARFI model could be a robust, new way to evaluate anisotropic BOLD signal changes in WM.

Effect sizes of BOLD CVR, resting-state signal fluctuations and time delay measures for the assessment of hemodynamic impairment in carotid occlusion patients

Background and purposeThe BOLD signal amplitude as a response to a hypercapnia stimulus is commonly used to assess cerebrovascular reserve. Despite recent advances, the implementation remains cumbersome and alternative ways to assess hemodynamic impairment are desirable. Resting-state BOLD signal fluctuations (rsBOLD) have been proposed however data on its sensitivity and dependence on baseline venous cerebral blood volume (vCBV) is limited. The primary aim of this study was to compare the effect sizes of resting-state and hypercapnia induced BOLD signal changes in the detection of hemodynamic impairment. The second aim of the study was to assess the dependence of BOLD signal variability on vCBV. Materials and methodsFifteen patients with internal carotid artery occlusive disease and 15 matched healthy controls were included in this study. The BOLD signal was derived from a dual-echo gradient-echo echo-planar sequence during hypercapnia (HC) and hyperoxia (HO) gas modulations. BOLD (fractional) amplitude of low frequency fluctuations ((f)ALFF) was compared to HC-BOLD, BOLD response delays derived from time delay analysis and ΔBOLD in response to progressively increasing HC. Effect sizes (i.e. the standard mean difference between patients and controls) were calculated. HO-BOLD was used to estimate vCBV, and its contribution to the variability in rsBOLD signal was evaluated. ResultsThe effect sizes of ALFF and fALFF (0.61 and 0.72) were lower than the effect sizes related to hypercapnia-based hemodynamic assessment analysis; 1.62, 1.56 and 0.90 for HC-BOLD, BOLD response delays and ΔBOLD in response to progressively increasing HC. A moderate relation was found between (f)ALFF and HC-BOLD in controls (R2 of 0.61 and 0.42), but this relation decreased in patients (R2 of 0.33 and 0.15). (f)ALFF did not differ between patients and controls whereas HC-BOLD did (p < 0.005). The ΔBOLD response to progressively increasing HC was significantly different in between patients and controls for ΔEtCO2 values ≥ 2 mmHg (at +2 mmHg F(1, 18) = 5.85, p = 0.026). Up to 31% and 53% of the variance in the ALFF and HC-BOLD spatial distribution could be explained by HO-BOLD. ConclusionALFF and fALFF demonstrated a moderate effect size to detect hemodynamic impairment whereas the effect size was large for methods employing a hypercapnia-based vascular stress stimulus. Based on our analysis of BOLD signal change as a response to a progressively increasing hypercapnia stimulus we can argue that a hypercapnia stimulus of at least 2 mmHg above baseline EtCO2 is necessary to evaluate hemodynamic impairment. We also demonstrated that a substantial amount of information imbedded in the rsBOLD and HC-BOLD was explained by HO-BOLD. HO-BOLD can serve as a proxy for vCBV and this thus indicates that one should be careful when adopting these techniques in disease cases with compromised CBV.

S133. Resting State Bold Signal Variability Correlates With Clinical Dimensions in Euthymic Bipolar Patients

S133. Resting State Bold Signal Variability Correlates With Clinical Dimensions in Euthymic Bipolar Patients

Altered Local Spatiotemporal Consistency of Resting-State BOLD Signals in Patients with Generalized Tonic-Clonic Seizures.

The purpose of this study was to evaluate the spatiotemporal Consistency of spontaneous activities in local brain regions in patients with generalized tonic-clonic seizures (GTCS). The resting-state fMRI data were acquired from nineteen patients with GTCS and twenty-two matched healthy subjects. FOur-dimensional (spatiotemporal) Consistency of local neural Activities (FOCA) metric was used to analyze the spontaneous activity in whole brain. The FOCA difference between two groups were detected using a two sample t-test analysis. Correlations between the FOCA values and features of seizures were analyzed. The findings of this study showed that patients had significantly increased FOCA in motor-related cortex regions, including bilateral supplementary motor area, paracentral lobule, precentral gyrus and left basal ganglia, as well as a substantial reduction of FOCA in regions of default mode network (DMN) and parietal lobe. In addition, several brain regions in DMN demonstrated more reduction with longer duration of epilepsy and later onset age, and the motor-related regions showed higher FOCA value in accompany with later onset age. These findings implicated the abnormality of motor-related cortical network in GTCS which were associated with the genesis and propagation of epileptiform activity. And the decreased FOCA in DMN might reflect the intrinsic disturbance of brain activity. Moreover, our study supported that the FOCA might be potential tool to investigate local brain spontaneous activity related with the epileptic activity, and to provide important insights into understanding the underlying pathophysiological mechanisms of GTCS.

GP.05 Sensory-motor network functional connectivity in hemiparetic children with perinatal stroke

Background: Perinatal stroke is the most common cause of hemiparetic cerebral palsy. Post-stroke plasticity is well studied in adults, but mechanisms in children are poorly understood. To better understand the relationship between functional connectivity and disability, we used rsfMRI to compare connectivity with sensorimotor dysfunction. Methods: Subjects with periventricular venous infarction were compared to controls. Resting-state BOLD signal was acquired on 3T MRI and analyzed using SPM12. Functional connectivity was computed between S1 and M1 of the left/non-lesioned and right/ lesioned hemisphere. Primary outcome was connectivity expressed as a Pearson correlation coefficient. Motor function was measured using the Assisting Hand Assessment (AHA), and Melbourne Assessment (MA). Proprioceptive function was measured using a robotic position matching task (VarXY). Results: Subjects included 17 PVI and 21 controls. AHA and MA in patients were negatively correlated with connectivity (increased connectivity=poorer performance). Correlations between AHA and connectivity between non-lesioned M1 to bilateral S1s were significant. VarXY in PVI was inversely correlated with connectivity (increased connectivity=improved performance), significantly between non-lesioned S1 and bilateral M1s. Control VarXY was positively correlated with connectivity between non-dominant S1 to bilateral M1s. Conclusions: We demonstrated significant correlations between connectivity and motor/sensory function in PVI patients. Greater insight into understanding reorganization of brain networks following perinatal stroke may facilitate personalized rehabilitation.

Fractional amplitude of low-frequency fluctuations (fALFF) in post-stroke depression.

Depression is a common outcome following stroke, associated with reduced quality of life and poorer recovery. Despite attempts to associate depression symptoms with specific lesion sites, the neural basis of post-stroke depression remains poorly understood. Resting state fMRI has provided new insights into the neural underpinnings of post-stroke depression, but has been limited to connectivity analyses exploring interregional correlations in the time-course of activity. Other aspects of resting state BOLD signal remain unexamined. Measuring the amplitude of low frequency fluctuations allows the detection of spontaneous neural activity across the whole brain. It provides complementary information about frequency-specific local neural activity.We calculated the fractional amplitude of low frequency fluctuations (fALFF) in a group of 64 participants scanned 3 months post-stroke. Twenty showed depression symptoms when assessed with the Patient Health Questionnaire (PHQ-9). We performed analyses in both the typical 0.01–0.08 Hz range, as well as separately in the slow-5 (0.01–0.027 Hz) and slow-4 (0.027–0.073 Hz) ranges. We found significantly higher fALFF in the depressed compared to non-depressed participants in the left dorsolateral prefrontal cortex (DLPFC) and the right precentral gyrus, and a significant association between higher depression scores and higher fALFF in the left insula. The group differences were detected in the slow-5 fluctuations, while the association with depression severity was observed in the slow-4 range. We conclude that post-stroke depression can be characterised by aberrant spontaneous local neural activity, which in small samples could be a more sensitive measure than lesion volume and location.