Modality-Dependent Impact of Hallucinations on Low-Frequency Fluctuations in Schizophrenia

Hare, Stephanie; Ford, Judith M.; Ahmadi, A; Damaraju, Eswar; Belger, Ayşenil; Bustillo, Juan; Hj, Lee; Mathalon, Daniel H.; Mueller, Bryon A.; Preda, Adrian; Erp, Theo G.M. van; Potkin, Steven G.; Calhoun, Vince D.; Turner, Jessica A.

doi:10.1093/schbul/sbw093

Cited by 36 publications

(37 citation statements)

References 31 publications

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“…In addition, Fryer et al used voxel wise amplitude of low-frequency fluctuations (ALFF) between frequencies 0.01-0.08 to find that SZs have lower ALFF compared to TCs especially in posterior cortex, occipital and cerebellar lobes (Fryer, et al, 2016). This observation has been reported in other studies as well (Alonso-Solis, et al, 2017;Calhoun, et al, 2012;Hare, et al, 2017;Hoptman, et al, 2010). On the other hand, Alonso-Solis et al report that ALFF for SZ is higher than that of TCs in insula (Alonso-Solis, et al, 2017).…”

Section: Schizophrenia-related Findingsmentioning

confidence: 87%

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

Faghiri

Iraji

Damaraju

et al. 2019

Preprint

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Studying dynamic functional connectivity (dFC) has been the focus of many studies in recent years. The most commonly used estimator for dFC uses a sliding window in combination with a connectivity estimator such as Pearson correlation. Here, we propose a new approach to estimate connectivity while preserving its full frequency range and subsequently examine both static and dynamic connectivity in one unified approach. This approach which we call filter banked connectivity (FBC), implements frequency tiling directly in the connectivity domain contrary to other studies where frequency tiling is done in the activity domain. This leads to more accurate modeling, and a unified approach to capture connectivity ranging from static to highly dynamic, avoiding the need to pick a specific band as in a sliding window approach.First, we demonstrated that our proposed approach, can estimate connectivity at frequencies that sliding window approach fails. Next we evaluated the ability of the approach to identify group differences by using the FBC approach to estimate dFNC in a resting fMRI data set including schizophrenia patients (SZ, n=151) and typical controls (TC, n=163). To summarize the results, we used k-means to cluster the FBC values into different clusters. Some states showed very weak low frequency strength and as such SWPC was not well suited to capture them. Additionally, we found that SZs tend to spend more time in states exhibiting higher frequencies and engaging the default mode network and its anticorrelations with other networks compared to TCs which spent more time in lower frequency states which primarily includes strong intercorrelations within the sensorimotor domains. In summary, the proposed approach offers a novel way to estimate connectivity while unifying static and dynamic connectivity analyses and can provide additional otherwise missed information about the frequency profile of connectivity patterns.

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Section: Schizophrenia-related Findingsmentioning

confidence: 87%

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

Faghiri

Iraji

Damaraju

et al. 2019

Preprint

Self Cite

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“…One important strength of this study was assessing variability separately at slow4 and slow5. Frequency-dependent variability alterations in region-specific brain areas have been reported at slow4 and slow5 in different disorders [41][42][43][44][45][46] . These results suggest both disease-and frequency-dependent disruptions of variability patterns.…”

Section: Discussionmentioning

confidence: 99%

Distinct BOLD variability changes in the default mode and salience networks in Alzheimer’s disease spectrum and associations with cognitive decline

Zhang

Zuo

et al. 2020

Sci Rep

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However, it remains largely unknown how network-specific and frequency-specific variability changes along the Alzheimer's disease (AD) spectrum and relates to cognitive decline. We hypothesized that cognitive impairment was related to distinct BOLD variability alterations in two brain networks with reciprocal relationship, i.e., the AD-specific default mode network (DMN) and the salience network (SN). We examined variability of resting-state fMRI data at two characteristic slow frequency-bands of slow4 (0.027-0.073 Hz) and slow5 (0.01-0.027 Hz) in 96 AD, 98 amnestic mild cognitive impairment (aMCI), and 48 age-matched healthy controls (HC) using two commonly used pre-processing pipelines. Cognition was measured with a neuropsychological assessment battery. Using both global signal regression (GSR) and independent component analysis (ICA), results generally showed a reciprocal DMN-SN variability balance in aMCI (vs. AD and/or HC), although there were distinct frequency-specific variability patterns in association with different pre-processing approaches. Importantly, lower slow4 posterior-DMN variability correlated with poorer baseline cognition/smaller hippocampus and predicted faster cognitive decline in all patients using both GSR and ICA. Altogether, our findings suggest that reciprocal DMN-SN variability balance in aMCI might represent an early signature in neurodegeneration and cognitive decline along the AD spectrum.Alzheimer's disease (AD) is the major cause of dementia, and increasing attention has been focused on early disease detection/prevention. Therefore, studying brain changes along the AD disease continuum is important, i.e., from normal aging to the prodromal stage (amnestic mild cognitive impairment, aMCI) and finally to dementia stage. Using resting-state functional connectivity methods 1-5 that quantifies the temporal synchrony between brain regions, both AD and aMCI have been found to target large-scale networks, including reduced After ICA-based denoising, there was a main effect of group mainly in the posterior DMN including the precuneus/posterior cingulate cortex and VN (cuneus), and the SN (insula) ( Supplementary Table 1). Group comparisons replicated the GSR findings that aMCI had higher variability in the posterior DMN compared with AD (precuneus; Fig. 1B, bottom panel) and HC (angular gyrus ; Fig. 1A, bottom panel), as well as lower SN variability compared with HC (insula ; Fig. 1C, bottom panel), although the latter did not survive the cluster-level Scientific RepoRtS | (2020) 10:6457 | https://doi.Furthermore, we examined whether the variability related to global cognition decline over time, defined as the difference between baseline and year 2 (year 2 minus baseline).For all correlation analyses, we focused on the brain clusters showing group differences between aMCI and HC or between AD and HC (including clusters surviving the voxel-level threshold consistently using both GSR and ICA-based denoising) (n = 3 for slow4, and n = 6 for slow5 for both data denoising methods). Correlati...

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“…ALFF, which quantifies local resting‐state signal fluctuations, was calculated as the integral of signal amplitude in the low‐frequency domain (0.009–0.08 Hz) [Zou et al, ]. The subject‐level voxelwise ALFF map was converted into a z‐score map by subtracting the mean ALFF of the whole brain and dividing by the standard deviation [Hare et al, ; Zang et al, ; Zou et al, 2010, ]. We decomposed the ALFF calculation in the low‐frequency range into two bands as previously described [Buzsaki and Draguhn, ; Han et al, ; Hare et al, ; Zuo et al, ], i.e., slow‐4 (ALFFs4, 0.027–0.073 Hz) and slow‐5 (ALFFs5, 0.01–0.027 Hz).…”

Section: Methodsmentioning

confidence: 99%

“…Resting‐state fMRI has emerged as a powerful tool for mapping the spontaneous functional activity of the human brain noninvasively, and it is particularly useful for clinical populations, such as ABI patients [Di Perri et al, ; Sharp et al, ]. The amplitude of low‐frequency fluctuation (ALFF, generally in the range of 0.01–0.08 Hz) [Zang et al, ] is an efficient index that quantifies resting‐state local spontaneous neuronal activity, reflects the regional metabolic level of glucose [Aiello et al, ], shows high test–retest reliability particularly suitable for longitudinal observations [Zuo et al, ], and has been widely adopted in clinical studies [Han et al, ; Hare et al, ; Lui et al, ; Meda et al, ] which closely correlates with clinical symptoms [Hare et al, ; Lui et al, ]. By decomposing the low‐frequency fluctuations into slow‐4 (0.027–0.073 Hz) and slow‐5 (0.01–0.027 Hz) frequency bands consistent with Buzkasi's theory [Buzsaki and Draguhn, ], Meda and colleagues reported that ALFF in slow‐4 and slow‐5 might play complementary roles underlying the physiologic mechanisms for psychosis [Meda et al, ].…”

Section: Introductionmentioning

confidence: 99%

Longitudinal recovery of local neuronal activity and consciousness level in acquired brain injury

Zou

et al. 2017

Human Brain Mapping

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Decreased brain activity in the default mode network, particularly in the precuneus (PCU), has been consistently shown in acquired brain injury (ABI) patients. However, it is unclear whether resting-state brain activity recovers longitudinally in ABI patients and whether functional activity restoration is associated with improvements in consciousness level. Here, resting-state fMRI data were acquired from 23 ABI patients and 30 age- and gender-matched controls with two longitudinal observations for each participant. The fMRI data were analyzed using amplitude of low-frequency fluctuation (ALFF) to measure the fluctuation strength of local spontaneous activity, and seed-based functional connectivity was used to measure functional relationship with the seed region in the whole brain. The level of consciousness was assessed using the Glasgow Coma Scale (GCS) and Coma Recovery Scale-Revised (CRS-R) on both scanning days of the patients. Interaction effect between the two groups and two scans in ALFF was observed in the PCU, which was driven by restored ALFF in the ABI, while a stable ALFF in the control group. Moreover, restoration of ALFF in the PCU correlated with improvements in both the CRS-R and GCS. Specifically, recovery of ALFF in the PCU primarily reflected the signals of the slow-4 frequency band (0.027-0.073 Hz). Based on the functional connectivity maps of the PCU, we observed a nonsignificant interaction effect or correlation with consciousness level. These findings suggest local activity in the PCU but possibly not its functional connectivity, is related to the longitudinal changes in behavioral responsiveness in ABI. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

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Modality-Dependent Impact of Hallucinations on Low-Frequency Fluctuations in Schizophrenia

Cited by 36 publications

References 31 publications

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

Distinct BOLD variability changes in the default mode and salience networks in Alzheimer’s disease spectrum and associations with cognitive decline

Longitudinal recovery of local neuronal activity and consciousness level in acquired brain injury

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