Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data

The ultimate goal of brain connectivity studies is to propose, test, modify, and compare certain directional brain pathways. Path analysis or structural equation modeling (SEM) is an ideal statistical method for such studies. In this work, we propose a two-stage unified SEM plus GLM (General Linear Model) approach for the analysis of multisubject, multivariate functional magnetic resonance imaging (fMRI) time series data with subject-level covariates. In Stage 1, we analyze the fMRI multivariate time series for each subject individually via a unified SEM model by combining longitudinal pathways represented by a multivariate autoregressive (MAR) model, and contemporaneous pathways represented by a conventional SEM. In Stage 2, the resulting subject-level path coefficients are merged with subject-level covariates such as gender, age, IQ, etc., to examine the impact of these covariates on effective connectivity via a GLM. Our approach is exemplified via the analysis of an fMRI visual attention experiment. Furthermore, the significant path network from the unified SEM analysis is compared to that from a conventional SEM analysis without incorporating the longitudinal information as well as that from a Dynamic Causal Modeling (DCM) approach.     

Dynamic regional phase synchrony (DRePS)

Dynamic functional brain connectivity analysis is a fast expanding field in computational neuroscience research with the promise of elucidating brain network interactions. Sliding temporal window based approaches are commonly used in order to explore dynamic behavior of brain networks in task‐free functional magnetic resonance imaging (fMRI) data. However, the low effective temporal resolution of sliding window methods fail to capture the full dynamics of brain activity at each time point. These also require subjective decisions regarding window size and window overlap. In this study, we introduce dynamic regional phase synchrony (DRePS), a novel analysis approach that measures mean local instantaneous phase coherence within adjacent fMRI voxels. We evaluate the DRePS framework on simulated data showing that the proposed measure is able to estimate synchrony at higher temporal resolution than sliding windows of local connectivity. We applied DRePS analysis to task‐free fMRI data of 20 control subjects, revealing ultra‐slow dynamics of local connectivity in different brain areas. Spatial clustering based on the DRePS feature time series reveals biologically congruent local phase synchrony networks (LPSNs). Taken together, our results demonstrate three main findings. Firstly, DRePS has increased temporal sensitivity compared to sliding window correlation analysis in capturing locally synchronous events. Secondly, DRePS of task‐free fMRI reveals ultra‐slow fluctuations of ～0.002–0.02 Hz. Lastly, LPSNs provide plausible spatial information about time‐varying brain local phase synchrony. With the DRePS method, we introduce a framework for interrogating brain local connectivity, which can potentially provide biomarkers of human brain function in health and disease. Hum Brain Mapp 37:1970–1985, 2016. © 2016 Wiley Periodicals, Inc.     

Longitudinal resting state fMRI analysis in healthy controls and premanifest Huntington's disease gene carriers: A three‐year follow‐up study

Background: We previously demonstrated that in the premanifest stage of Huntington's disease (preHD), a reduced functional connectivity exists compared to healthy controls. In the current study, we look at possible changes in functional connectivity occurring longitudinally over a period of 3 years, with the aim of assessing the potential usefulness of this technique as a biomarker for disease progression in preHD. Methods: Twenty‐two preHD and 17 healthy control subjects completed resting state functional magnetic resonance imaging (fMRI) scans in two visits with 3 years in between. Differences in resting state connectivity were examined for eight networks of interest using FSL with three different analysis types: a dual regression method, region of interest approach, and an independent component analysis. To evaluate a possible combined effect of gray matter volume change and the change in blood oxygenation level dependent signal, the analysis was performed with and without voxel‐wise correction for gray matter volume. To evaluate possible correlations between functional connectivity change and the predicted time to disease onset, the preHD group was classed as preHD‐A if ≥10.9 years and preHD‐B if <10.9 years from predicted disease onset. Possible correlations between burden of pathology score and functional connectivity change in preHD were also assessed. Finally, longitudinal change in whole brain and striatal volumetric measures was assessed in the studied cohort. Results: Longitudinal analysis of the resting state‐fMRI (RS‐fMRI) data revealed no differences in the degree of connectivity change between the groups over a period of 3 years, though a significantly higher rate of striatal atrophy was found in the preHD group compared to controls in the same period. Discussion: Based on the results found in this study, the provisional conclusion is that RS‐fMRI lacks sensitivity in detecting changes in functional connectivity in HD gene carriers prior to disease manifestation over a 3‐year follow‐up period. Hum Brain Mapp, 36:110–119, 2015. © 2014 Wiley Periodicals, Inc.     

Parcellation of the human hippocampus based on gray matter volume covariance: Replicable results on healthy young adults

The hippocampus is a key brain region that participates in a range of cognitive and affective functions, and is involved in the etiopathogenesis of numerous neuropsychiatric disorders. The structural complexity and functional diversity of the hippocampus suggest the existence of structural and functional subdivisions within this structure. For the first time, we parcellated the human hippocampus with two independent data sets, each of which consisted of 198 T1‐weighted structural magnetic resonance imaging (sMRI) images of healthy young subjects. The method was based on gray matter volume (GMV) covariance, which was quantified by a bivariate voxel‐to‐voxel linear correlation approach, as well as a multivariate masked independent component analysis approach. We subsequently interrogated the relationship between the GMV covariance patterns and the functional connectivity patterns of the hippocampal subregions using sMRI and resting‐state functional MRI (fMRI) data from the same participants. Seven distinct GMV covariance‐based subregions were identified for bilateral hippocampi, with robust reproducibility across the two data sets. We further demonstrated that the structural covariance patterns of the hippocampal subregions had a correspondence with the intrinsic functional connectivity patterns of these subregions. Together, our results provide a topographical configuration of the hippocampus with converging structural and functional support. The resulting subregions may improve our understanding of the hippocampal connectivity and functions at a subregional level, which provides useful parcellations and masks for future neuroscience and clinical research on the structural and/or functional connectivity of the hippocampus.     

Resting-state fMRI functional connectivity: a new perspective to evaluate pain modulation in migraine?

Resting-state (RS) functional magnetic resonance imaging (fMRI) is a relatively novel tool which explores connectivity between functionally linked, but anatomically separated, brain regions. The use of this technique has allowed the identification, at rest, of the main brain functional networks without requiring subjects to perform specific active tasks. Methodologically, several approaches can be applied for the analysis of RS fMRI, including seed-based, independent component analysis-based and/or cluster-based methods. The most consistently described RS network is the so-called “default mode network”. Using RS fMRI, several studies have identified functional connectivity abnormalities in migraine patients, mainly located at the level of the pain-processing network. RS functional connectivity is generally increased in pain-processing network, whereas is decreased in pain modulatory circuits. Significant abnormalities of RS functional connectivity occur also in affective networks, the default mode network and the executive control network. These results provide a strong characterization of migraine as a brain dysfunction affecting intrinsic connectivity of brain networks, possibly reflecting the impact of long lasting pain on brain function.     

Spatiotemporal Reconfiguration of Large-Scale Brain Functional Networks during Propofol-Induced Loss of Consciousness

Applying graph theoretical analysis of spontaneous BOLD fluctuations in functional magnetic resonance imaging (fMRI), we investigated whole-brain functional connectivity of 11 healthy volunteers during wakefulness and propofol-induced loss of consciousness (PI-LOC). After extraction of regional fMRI time series from 110 cortical and subcortical regions, we applied a maximum overlap discrete wavelet transformation and investigated changes in the brain's intrinsic spatiotemporal organization. During PI-LOC, we observed a breakdown of subcortico-cortical and corticocortical connectivity. Decrease of connectivity was pronounced in thalamocortical connections, whereas no changes were found for connectivity within primary sensory cortices. Graph theoretical analyses revealed significant changes in the degree distribution and local organization metrics of brain functional networks during PI-LOC: compared with a random network, normalized clustering was significantly increased, as was small-worldness. Furthermore we observed a profound decline in long-range connections and a reduction in whole-brain spatiotemporal integration, supporting a topological reconfiguration during PI-LOC. Our findings shed light on the functional significance of intrinsic brain activity as measured by spontaneous BOLD signal fluctuations and help to understand propofol-induced loss of consciousness.     

Functional network estimation using multigraph learning with application to brain maturation study

Although most dramatic structural changes occur in the perinatal period, a growing body of evidences demonstrates that adolescence and early adulthood are also important for substantial neurodevelopment. We were thus motivated to explore brain development during puberty by evaluating functional connectivity network (FCN) differences between childhood and young adulthood using multi‐paradigm task‐based functional magnetic resonance imaging (fMRI) measurements. Different from conventional multigraph based FCN construction methods where the graph network was built independently for each modality/paradigm, we proposed a multigraph learning model in this work. It promises a better fitting to FCN construction by jointly estimating brain network from multi‐paradigm fMRI time series, which may share common graph structures. To investigate the hub regions of the brain, we further conducted graph Fourier transform (GFT) to divide the fMRI BOLD time series of a node within the brain network into a range of frequencies. Then we identified the hub regions characterizing brain maturity through eigen‐analysis of the low frequency components, which were believed to represent the organized structures shared by a large population. The proposed method was evaluated using both synthetic and real data, which demonstrated its effectiveness in extracting informative brain connectivity patterns. We detected 14 hub regions from the child group and 12 hub regions from the young adult group. We show the significance of these findings with a discussion of their functions and activation patterns as a function of age. In summary, our proposed method can extract brain connectivity network more accurately by considering the latent common structures between different fMRI paradigms, which are significant for both understanding brain development and recognizing population groups of different ages.     

Functional connectivity‐based parcellation of amygdala using self‐organized mapping: A data driven approach

The overall goal of this work is to demonstrate how resting state functional magnetic resonance imaging (fMRI) signals may be used to objectively parcellate functionally heterogeneous subregions of the human amygdala into structures characterized by similar patterns of functional connectivity. We hypothesize that similarity of functional connectivity of subregions with other parts of the brain can be a potential basis to segment and cluster voxels using data driven approaches. In this work, self‐organizing map (SOM) was implemented to cluster the connectivity maps associated with each voxel of the human amygdala, thereby defining distinct subregions. The functional separation was optimized by evaluating the overall differences in functional connectivity between the subregions at group level. Analysis of 25 resting state fMRI data sets suggests that SOM can successfully identify functionally independent nuclei based on differences in their inter subregional functional connectivity, evaluated statistically at various confidence levels. Although amygdala contains several nuclei whose distinct roles are implicated in various functions, our objective approach discerns at least two functionally distinct volumes comparable to previous parcellation results obtained using probabilistic tractography and cytoarchitectonic analysis. Association of these nuclei with various known functions and a quantitative evaluation of their differences in overall functional connectivity with lateral orbital frontal cortex and temporal pole confirms the functional diversity of amygdala. The data driven approach adopted here may be used as a powerful indicator of structure–function relationships in the amygdala and other functionally heterogeneous structures as well. Hum Brain Mapp 35:1247–1260, 2014. © 2013 Wiley Periodicals, Inc.     

Feature-space clustering for fMRI meta-analysis

Clustering functional magnetic resonance imaging (fMRI) time series has emerged in recent years as a possible alternative to parametric modeling approaches. Most of the work so far has been concerned with clustering raw time series. In this contribution we investigate the applicability of a clustering method applied to features extracted from the data. This approach is extremely versatile and encompasses previously published results [Goutte et al., 1999] as special cases. A typical application is in data reduction: as the increase in temporal resolution of fMRI experiments routinely yields fMRI sequences containing several hundreds of images, it is sometimes necessary to invoke feature extraction to reduce the dimensionality of the data space. A second interesting application is in the meta-analysis of fMRI experiment, where features are obtained from a possibly large number of single-voxel analyses. In particular this allows the checking of the differences and agreements between different methods of analysis. Both approaches are illustrated on a fMRI data set involving visual stimulation, and we show that the feature space clustering approach yields nontrivial results and, in particular, shows interesting differences between individual voxel analysis performed with traditional methods.     

Polymicrogyric Cortex may Predispose to Seizures via Abnormal Network Topology: An fMRI Connectomics Study

Polymicrogyria is a significant malformation of cortical development with a high incidence of epilepsy and cognitive deficits. Graph theoretic analysis is a useful approach to studying network organization in brain disorders. In this study, we used task‐free functional magnetic resonance imaging (fMRI) data from four patients with polymicrogyria and refractory epilepsy. Gray matter masks from structural MRI data were parcellated into 1,024 network nodes. Functional “connectomes” were obtained based on fMRI time series between the parcellated network nodes; network analysis was conducted using clustering coefficient, path length, node degree, and participation coefficient. These graph metrics were compared between nodes within polymicrogyric cortex and normal brain tissue in contralateral homologous cortical regions. Polymicrogyric nodes showed significantly increased clustering coefficient and characteristic path length. This is the first study using functional connectivity analysis in polymicrogyria—our results indicate a shift toward a regular network topology in polymicrogyric nodes. Regularized network topology has been demonstrated previously in patients with focal epilepsy and during focal seizures. Thus, we postulate that these network alterations predispose to seizures and may be relevant to cognitive deficits in patients with polymicrogyria.     

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.     

Motion correction and the use of motion covariates in multiple-subject fMRI analysis

The impact of using motion estimates as covariates of no interest was examined in general linear modeling (GLM) of both block design and rapid event-related functional magnetic resonance imaging (fMRI) data. The purpose of motion correction is to identify and eliminate artifacts caused by task-correlated motion while maximizing sensitivity to true activations. To optimize this process, a combination of motion correction approaches was applied to data from 33 subjects performing both a block-design and an event-related fMRI experiment, including analysis: (1) without motion correction; (2) with motion correction alone; (3) with motion-corrected data and motion covariates included in the GLM; and (4) with non-motion-corrected data and motion covariates included in the GLM. Inclusion of covariates was found to be generally useful for increasing the sensitivity of GLM results in the analysis of event-related data. When motion parameters were included in the GLM for event-related data, it made little difference if motion correction was actually applied to the data. For the block design, inclusion of motion covariates had a deleterious impact on GLM sensitivity when even moderate correlation existed between motion and the experimental design. Based on these results, we present a general strategy for block designs, event-related designs, and hybrid designs to identify and eliminate probable motion artifacts while maximizing sensitivity to true activations.     

Modulation of simultaneously collected hemodynamic and electrophysiological functional connectivity by ketamine and midazolam

The pharmacological modulation of functional connectivity in the brain may underlie therapeutic efficacy for several neurological and psychiatric disorders. Functional magnetic resonance imaging (fMRI) provides a noninvasive method of assessing this modulation, however, the indirect nature of the blood‐oxygen level dependent signal restricts the discrimination of neural from physiological contributions. Here we followed two approaches to assess the validity of fMRI functional connectivity in developing drug biomarkers, using simultaneous electroencephalography (EEG)/fMRI in a placebo‐controlled, three‐way crossover design with ketamine and midazolam. First, we compared seven different preprocessing pipelines to determine their impact on the connectivity of common resting‐state networks. Independent components analysis (ICA)‐denoising resulted in stronger reductions in connectivity after ketamine, and weaker increases after midazolam, than pipelines employing physiological noise modelling or averaged signals from cerebrospinal fluid or white matter. This suggests that pipeline decisions should reflect a drug's unique noise structure, and if this is unknown then accepting possible signal loss when choosing extensive ICA denoising pipelines could engender more confidence in the remaining results. We then compared the temporal correlation structure of fMRI to that derived from two connectivity metrics of EEG, which provides a direct measure of neural activity. While electrophysiological estimates based on the power envelope were more closely aligned to BOLD signal connectivity than those based on phase consistency, no significant relationship between the change in electrophysiological and hemodynamic correlation structures was found, implying caution should be used when making cross‐modal comparisons of pharmacologically‐modulated functional connectivity.     

Reduced amygdala-orbitofrontal connectivity during moral judgments in youths with disruptive behavior disorders and psychopathic traits

We used functional magnetic resonance imaging (fMRI) to investigate dysfunction in the amygdala and orbitofrontal cortex in adolescents with disruptive behavior disorders and psychopathic traits during a moral judgment task. Fourteen adolescents with psychopathic traits and 14 healthy controls were assessed using fMRI while they categorized illegal and legal behaviors in a moral judgment implicit association task. fMRI data were then analyzed using random-effects analysis of variance and functional connectivity. Youths with psychopathic traits showed reduced amygdala activity when making judgments about legal actions and reduced functional connectivity between the amygdala and orbitofrontal cortex during task performance. These results suggest that psychopathic traits are associated with amygdala and orbitofrontal cortex dysfunction. This dysfunction may relate to previous findings of disrupted moral judgment in this population.     

Reduced amygdala–orbitofrontal connectivity during moral judgments in youths with disruptive behavior disorders and psychopathic traits

We used functional magnetic resonance imaging (fMRI) to investigate dysfunction in the amygdala and orbitofrontal cortex in adolescents with disruptive behavior disorders and psychopathic traits during a moral judgment task. Fourteen adolescents with psychopathic traits and 14 healthy controls were assessed using fMRI while they categorized illegal and legal behaviors in a moral judgment implicit association task. fMRI data were then analyzed using random-effects analysis of variance and functional connectivity. Youths with psychopathic traits showed reduced amygdala activity when making judgments about legal actions and reduced functional connectivity between the amygdala and orbitofrontal cortex during task performance. These results suggest that psychopathic traits are associated with amygdala and orbitofrontal cortex dysfunction. This dysfunction may relate to previous findings of disrupted moral judgment in this population.     

Establishing the resting state default mode network derived from functional magnetic resonance imaging tasks as an endophenotype: A twins study

The resting state default mode network (DMN) has been shown to characterize a number of neurological and psychiatric disorders. Evidence suggests an underlying genetic basis for this network and hence could serve as potential endophenotype for these disorders. Heritability is a defining criterion for endophenotypes. The DMN is measured either using a resting‐state functional magnetic resonance imaging (fMRI) scan or by extracting resting state activity from task‐based fMRI. The current study is the first to evaluate heritability of this task‐derived resting activity. 250 healthy adult twins (79 monozygotic and 46 dizygotic same sex twin pairs) completed five cognitive and emotion processing fMRI tasks. Resting state DMN functional connectivity was derived from these five fMRI tasks. We validated this approach by comparing connectivity estimates from task‐derived resting activity for all five fMRI tasks, with those obtained using a dedicated task‐free resting state scan in an independent cohort of 27 healthy individuals. Structural equation modeling using the classic twin design was used to estimate the genetic and environmental contributions to variance for the resting‐state DMN functional connectivity. About 9–41% of the variance in functional connectivity between the DMN nodes was attributed to genetic contribution with the greatest heritability found for functional connectivity between the posterior cingulate and right inferior parietal nodes (P < 0.001). Our data provide new evidence that functional connectivity measures from the intrinsic DMN derived from task‐based fMRI datasets are under genetic control and have the potential to serve as endophenotypes for genetically predisposed psychiatric and neurological disorders. Hum Brain Mapp 35:3893–3902, 2014. © 2014 Wiley Periodicals, Inc .     

Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains

Even in the absence of an experimental effect, functional magnetic resonance imaging (fMRI) time series generally demonstrate serial dependence. This colored noise or endogenous autocorrelation typically has disproportionate spectral power at low frequencies, i.e., its spectrum is (1/f)-like. Various pre-whitening and pre-coloring strategies have been proposed to make valid inference on standardised test statistics estimated by time series regression in this context of residually autocorrelated errors. Here we introduce a new method based on random permutation after orthogonal transformation of the observed time series to the wavelet domain. This scheme exploits the general whitening or decorrelating property of the discrete wavelet transform and is implemented using a Daubechies wavelet with four vanishing moments to ensure exchangeability of wavelet coefficients within each scale of decomposition. For (1/f)-like or fractal noises, e.g., realisations of fractional Brownian motion (fBm) parameterised by Hurst exponent 0 < H < 1, this resampling algorithm exactly preserves wavelet-based estimates of the second order stochastic properties of the (possibly nonstationary) time series. Performance of the method is assessed empirically using (1/f)-like noise simulated by multiple physical relaxation processes, and experimental fMRI data. Nominal type 1 error control in brain activation mapping is demonstrated by analysis of 13 images acquired under null or resting conditions. Compared to autoregressive pre-whitening methods for computational inference, a key advantage of wavelet resampling seems to be its robustness in activation mapping of experimental fMRI data acquired at 3 Tesla field strength. We conclude that wavelet resampling may be a generally useful method for inference on naturally complex time series.     

Realistic models of apparent dynamic changes in resting‐state connectivity in somatosensory cortex

Variations over time in resting‐state correlations in blood oxygenation level‐dependent (BOLD) signals from different cortical areas may indicate changes in brain functional connectivity. However, apparent variations over time may also arise from stationary signals when the sample duration is finite. Recently, a vector autoregressive (VAR) null model has been proposed to simulate real functional magnetic resonance imaging (fMRI) data, which provides a robust stationary model for identifying possible temporal dynamic changes in functional connectivity. In this work, we propose a simpler model that uses a filtered stationary dataset. The filtered stationary model generates statistically stationary time series from random data with a single prescribed correlation coefficient that is calculated as the average over the entire time series. In addition, we propose a dynamic model, which is better able to replicate real fMRI connectivity, estimated from monkey brain studies, than the two stationary models. We compare simulated results using these three models with the behavior of primary somatosensory cortex (S1) networks in anesthetized squirrel monkeys at high field (9.4 T), using a sliding window correlation analysis. We found that at short window sizes, both stationary models reproduced the distribution of correlations of real signals well, but at longer window sizes, a dynamic model reproduced the distribution of correlations of real signals better than the stationary models. While stationary models replicate several features of real data, a close representation of the behavior of resting‐state data acquired from somatosensory cortex of non‐human primates is obtained only when a dynamic correlation is introduced, suggesting dynamic variations in connectivity are real. Hum Brain Mapp 37:3897–3910, 2016. © 2016 Wiley Periodicals, Inc .     

Beware detrending: Optimal preprocessing pipeline for low‐frequency fluctuation analysis

Resting‐state functional magnetic resonance imaging (rs‐fMRI) offers the possibility to assess brain function independent of explicit tasks and individual performance. This absence of explicit stimuli in rs‐fMRI makes analyses more susceptible to nonneural signal fluctuations than task‐based fMRI. Data preprocessing is a critical procedure to minimise contamination by artefacts related to motion and physiology. We herein investigate the effects of different preprocessing strategies on the amplitude of low‐frequency fluctuations (ALFFs) and its fractional counterpart, fractional ALFF (fALFF). Sixteen artefact reduction schemes based on nuisance regression are applied to data from 82 subjects acquired at 1.5 T, 30 subjects at 3 T, and 23 subjects at 7 T, respectively. In addition, we examine test–retest variance and effects of bias correction. In total, 569 data sets are included in this study. Our results show that full artefact reduction reduced test–retest variance by up to 50%. Polynomial detrending of rs‐fMRI data has a positive effect on group‐level t‐values for ALFF but, importantly, a negative effect for fALFF. We show that the normalisation process intrinsic to fALFF calculation causes the observed reduction and introduce a novel measure for low‐frequency fluctuations denoted as high‐frequency ALFF (hfALFF). We demonstrate that hfALFF values are not affected by the negative detrending effects seen in fALFF data. Still, highest grey matter (GM) group‐level t‐values were obtained for fALFF data without detrending, even when compared to an exploratory detrending approach based on autocorrelation measures. From our results, we recommend the use of full nuisance regression including polynomial detrending in ALFF data, but to refrain from using polynomial detrending in fALFF data. Such optimised preprocessing increases GM group‐level t‐values by up to 60%.