Abstract:Functional magnetic resonance imaging (fMRI) of the blood oxygen level dependent (BOLD) response has commonly been used to investigate the neuropathology underlying cognitive and sensory deficits in patients with schizophrenia (SP) by examining the positive phase of the BOLD response, assuming a fixed shape for the hemodynamic response function (HRF). However, the individual phases (positive and post-stimulus undershoot (PSU)) of the HRF may be differentially affected by a variety of underlying pathologies. Th… Show more
“…For this task, the target stimuli consisted of a simultaneously presented blue box (visual angle = 4.3 × 6.5 ○ ) and auditory tone (2000 Hz re-sampled with a 10 ms linear ramp) for 300 ms duration. Participants responded with a button press upon the detection of the stimuli (Hanlon et al 2016 ). Fig.…”
In this paper we describe an open-access collection of multimodal neuroimaging data in schizophrenia for release to the community. Data were acquired from approximately 100 patients with schizophrenia and 100 age-matched controls during rest as well as several task activation paradigms targeting a hierarchy of cognitive constructs. Neuroimaging data include structural MRI, functional MRI, diffusion MRI, MR spectroscopic imaging, and magnetoencephalography. For three of the hypothesis-driven projects, task activation paradigms were acquired on subsets of ~200 volunteers which examined a range of sensory and cognitive processes (e.g., auditory sensory gating, auditory/visual multisensory integration, visual transverse patterning). Neuropsychological data were also acquired and genetic material via saliva samples were collected from most of the participants and have been typed for both genome-wide polymorphism data as well as genome-wide methylation data. Some results are also presented from the individual studies as well as from our data-driven multimodal analyses (e.g., multimodal examinations of network structure and network dynamics and multitask fMRI data analysis across projects). All data will be released through the Mind Research Network’s collaborative informatics and neuroimaging suite (COINS).Electronic supplementary materialThe online version of this article (doi:10.1007/s12021-017-9338-9) contains supplementary material, which is available to authorized users.
“…For this task, the target stimuli consisted of a simultaneously presented blue box (visual angle = 4.3 × 6.5 ○ ) and auditory tone (2000 Hz re-sampled with a 10 ms linear ramp) for 300 ms duration. Participants responded with a button press upon the detection of the stimuli (Hanlon et al 2016 ). Fig.…”
In this paper we describe an open-access collection of multimodal neuroimaging data in schizophrenia for release to the community. Data were acquired from approximately 100 patients with schizophrenia and 100 age-matched controls during rest as well as several task activation paradigms targeting a hierarchy of cognitive constructs. Neuroimaging data include structural MRI, functional MRI, diffusion MRI, MR spectroscopic imaging, and magnetoencephalography. For three of the hypothesis-driven projects, task activation paradigms were acquired on subsets of ~200 volunteers which examined a range of sensory and cognitive processes (e.g., auditory sensory gating, auditory/visual multisensory integration, visual transverse patterning). Neuropsychological data were also acquired and genetic material via saliva samples were collected from most of the participants and have been typed for both genome-wide polymorphism data as well as genome-wide methylation data. Some results are also presented from the individual studies as well as from our data-driven multimodal analyses (e.g., multimodal examinations of network structure and network dynamics and multitask fMRI data analysis across projects). All data will be released through the Mind Research Network’s collaborative informatics and neuroimaging suite (COINS).Electronic supplementary materialThe online version of this article (doi:10.1007/s12021-017-9338-9) contains supplementary material, which is available to authorized users.
“…We focused on a number of likely sources of heterogeneity in fMRI signals, using effects of age to illustrate some of our examples. This is based on recent evidence of group differences in signal autocorrelation , Arbabshirani et al (2014a)), HRF shape (Hanlon et al (2016), Huettel et al (2001), Aizenstein et al (2004), D'Esposito et al (1999), and non-neural physiological noise levels (Geerligs et al (2015), Mark et al (2015)). Nevertheless, our findings apply in any situation where such heterogeneity may arise between individuals.…”
Section: Discussionmentioning
confidence: 99%
“…Another example of heterogeneity is differences in the haemodynamic response function (HRF). The shape of the HRF, which can be modelled as a finite impulse response kernel, has been found to vary between healthy patients and patients with schizophrenia (Hanlon et al (2016)) and also between age groups (Huettel et al (2001), Aizenstein et al (2004), D'Esposito et al (1999). Even non-neural physiological noise levels might differ across groups, owing for example to greater within-scan head movement in old relative to young subjects (Geerligs et al (2015)).…”
Several methods have been developed to measure dynamic functional connectivity (dFC) in fMRI data. These methods are often based on a sliding-window analysis, which aims to capture how the brain's functional organization varies over the course of a scan. The aim of many studies is to compare dFC across groups, such as younger versus older people. However, spurious group differences in measured dFC may be caused by other sources of heterogeneity between people. For example, the shape of the haemodynamic response function (HRF) and levels of measurement noise have been found to vary with age. We use a generic simulation framework for fMRI data to investigate the effect of such heterogeneity on estimates of dFC. Our findings show that, despite no differences in true dFC, individual differences in measured dFC can result from other (non-dynamic) features of the data, such as differences in neural autocorrelation, HRF shape, connectivity strength and measurement noise. We also find that common dFC methods such as k-means and multilayer modularity approaches can detect spurious group differences in dynamic connectivity due to inappropriate setting of their hyperparameters. fMRI studies therefore need to consider alternative sources of heterogeneity across individuals before concluding differences in dFC.
“…HRF differences can also significantly confound resting-state fMRI functional connectivity analyses (Rangaprakash, Wu, Marinazzo, Hu, & Deshpande, 2018). If so, dystonia may belong to a growing number of neuropsychiatric disorders that manifest abnormal hemodynamic responses that includes autism (Yan, Rangaprakash, & Deshpande, 2018), post-traumatic stress disorder (Rangaprakash et al, 2017), schizophrenia (Hanlon et al, 2016), traumatic brain injury (Rangaprakash et al, 2017), and cerebrovascular disease (Para et al, 2017). Since CD shares many pathophysiological features with other types of focal dystonia (Defazio, Berardelli, & Hallett, 2007;Jinnah et al, 2013), changes to neurovascular coupling that lead to different BOLD response features could represent a broader neurophysiological confound in dystonia.…”
Section: Con Clus Ionsmentioning
confidence: 99%
“…Since CD shares many pathophysiological features with other types of focal dystonia (Defazio, Berardelli, & Hallett, 2007;Jinnah et al, 2013), changes to neurovascular coupling that lead to different BOLD response features could represent a broader neurophysiological confound in dystonia. If so, dystonia may belong to a growing number of neuropsychiatric disorders that manifest abnormal hemodynamic responses that includes autism (Yan, Rangaprakash, & Deshpande, 2018), post-traumatic stress disorder (Rangaprakash et al, 2017), schizophrenia (Hanlon et al, 2016), traumatic brain injury (Rangaprakash et al, 2017), and cerebrovascular disease (Para et al, 2017).…”
Neuroimaging studies using functional magnetic resonance imaging (fMRI), which measures brain activity by detecting the changes in blood oxygenation levels, are advancing our understanding of the pathophysiology of dystonia. Neurobiological disturbances in dystonia, however, may affect neurovascular coupling and impact the interpretability of fMRI studies. We evaluated here whether the hemodynamic response patterns during a behaviorally matched motor task are altered in isolated cervical dystonia (CD). Twenty-five CD patients and 25 healthy controls (HCs) underwent fMRI scanning during a paced finger tapping task (nondystonic task in patients).Imaging data were analyzed using a constrained principal component analysis-a statistical method that combines regression analysis and principal component analysis and enables the extraction of task-related functional networks and determination of the spatial and temporal hemodynamic response patterns associated with the task performance. Data from three patients and two controls were removed due to excessive movement. No significant differences in demographics or motor performance were observed. Three task-associated functional brain networks were identified.During task performance, reduced hemodynamic responses were seen in a sensorimotor network and in a network that included key nodes of the default mode, executive control and visual networks. During rest, reductions in hemodynamic responses were seen in the cognitive/visual network. Lower hemodynamic responses within the primary sensorimotor network in patients were correlated with the increased dystonia severity. Pathophysiological disturbances in isolated CD, such as alterations in inhibitory signaling and dopaminergic neurotransmission, may impact neurovascular coupling. Not accounting for hemodynamic response differences in fMRI studies of dystonia could lead to inaccurate results and interpretations.
K E Y W O R D SBOLD, cervical dystonia, finger tapping, fMRI, hemodynamic response | 693 BERMAN Et Al.
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