Correcting Task fMRI Signals for Variability in Baseline CBF Improves BOLD-Behavior Relationships: A Feasibility Study in an Aging Model

Krishnamurthy, Vivek; Krishnamurthy, Lisa C.; Drucker, Jonathan H.; Kundu, Suprateek; Ji, Bing; Hortman, Kyle; Roberts, Simone R.; Mammino, Kevin; Tran, Stella M.; Gopinath, Kaundinya; McGregor, Keith M.; Rodriguez, Amy D.; Qiu, Deqiang; Crosson, Bruce; Nocera, Joe

doi:10.3389/fnins.2020.00336

Cited by 13 publications

(31 citation statements)

References 62 publications

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“…From this viewpoint, we have attempted to undertake an integrative physiology approach (that is cerebrovascular control and its integration with other physiological systems such as cardiovascular and pulmonary) to describe the role of CVR, and how understanding the impairment of such mechanisms in various disease models can advance brain rehabilitation programs. Given the versatility of neuroimaging techniques, and that the cerebrovascular control is a complex interplay of various physiological components, it is important to underscore that future work should combine CVR imaging with other neuroimaging approaches that can measure blood flow along with BOLD activity ( Krishnamurthy et al, 2020 ), autoregulation ( Whittaker et al, 2019 ), and brain pH ( Ellingson et al, 2019 ). The future of rehabilitation lies in the search for biomarkers of disease status, progression, and treatment response.…”

Section: Discussionmentioning

confidence: 99%

“…Cerebrovascular reactivity can be retrospectively combined with task fMRI data to sensitize the BOLD signals to index neural activity ( Bandettini and Wong, 1997 ; Liu et al, 2013 ). Given that profiling and characterizing behavioral changes is important for cognitive neurorehabilitation, it is important to improve the relationship between neuro-sensitized BOLD maps and relevant behavior ( Krishnamurthy et al, 2020 ). Thus the methodological approach of combining CVR maps with BOLD data is extremely important, and from that perspective, systematic experimental and theoretic work from Liau and Liu (2009) have shown that normalization based on division (i.e., voxel-wise BOLD activity divided by corresponding CVR) approach resulted in increased inter-subject variability.…”

Section: Rehabilitation Relevant Advanced Imaging Approaches Involvinmentioning

confidence: 99%

“…Thus the methodological approach of combining CVR maps with BOLD data is extremely important, and from that perspective, systematic experimental and theoretic work from Liau and Liu (2009) have shown that normalization based on division (i.e., voxel-wise BOLD activity divided by corresponding CVR) approach resulted in increased inter-subject variability. On the other hand, group-level normalization by co-variate approach (i.e., voxel-wise covarying out CVR from BOLD activity at group level; Liau and Liu, 2009 ) retained task sensitivity and improved the predictability of behavior ( Krishnamurthy et al, 2020 ). Thus, the take home message is that mindful selection of BOLD sensitization methodologies is important to accurately interpret treatment-induced changes in rehabilitation.…”

Section: Rehabilitation Relevant Advanced Imaging Approaches Involvinmentioning

confidence: 99%

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The Utility of Cerebrovascular Reactivity MRI in Brain Rehabilitation: A Mechanistic Perspective

et al. 2021

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Cerebrovascular control and its integration with other physiological systems play a key role in the effective maintenance of homeostasis in brain functioning. Maintenance, restoration, and promotion of such a balance are one of the paramount goals of brain rehabilitation and intervention programs. Cerebrovascular reactivity (CVR), an index of cerebrovascular reserve, plays an important role in chemo-regulation of cerebral blood flow. Improved vascular reactivity and cerebral blood flow are important factors in brain rehabilitation to facilitate desired cognitive and functional outcomes. It is widely accepted that CVR is impaired in aging, hypertension, and cerebrovascular diseases and possibly in neurodegenerative syndromes. However, a multitude of physiological factors influence CVR, and thus a comprehensive understanding of underlying mechanisms are needed. We are currently underinformed on which rehabilitation method will improve CVR, and how this information can inform on a patient’s prognosis and diagnosis. Implementation of targeted rehabilitation regimes would be the first step to elucidate whether such regimes can modulate CVR and in the process may assist in improving our understanding for the underlying vascular pathophysiology. As such, the high spatial resolution along with whole brain coverage offered by MRI has opened the door to exciting recent developments in CVR MRI. Yet, several challenges currently preclude its potential as an effective diagnostic and prognostic tool in treatment planning and guidance. Understanding these knowledge gaps will ultimately facilitate a deeper understanding for cerebrovascular physiology and its role in brain function and rehabilitation. Based on the lessons learned from our group’s past and ongoing neurorehabilitation studies, we present a systematic review of physiological mechanisms that lead to impaired CVR in aging and disease, and how CVR imaging and its further development in the context of brain rehabilitation can add value to the clinical settings.

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Section: Discussionmentioning

confidence: 99%

Section: Rehabilitation Relevant Advanced Imaging Approaches Involvinmentioning

confidence: 99%

Section: Rehabilitation Relevant Advanced Imaging Approaches Involvinmentioning

confidence: 99%

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The Utility of Cerebrovascular Reactivity MRI in Brain Rehabilitation: A Mechanistic Perspective

et al. 2021

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“…We provided evidence that our novel TCMcorr methodology is more effective in mitigating TCM artifacts compared to other existing approaches, while also being cost efficient to reliably denoise larger data sets. From a clinical translation standpoint, optimal removal of TCM artifacts will allow for subsequent secondary analyses such as quantifying area under the curve (underneath the denoised HRF) to obtain longitudinal BOLD‐behavior relationships (Krishnamurthy, Krishnamurthy, Drucker, et al, 2020) indexing neuroplasticity in response to treatment and interventions.…”

Section: Discussionmentioning

confidence: 99%

A method to mitigate spatio‐temporally varying task‐correlated motion artifacts from overt‐speech fMRI paradigms in aphasia

Krishnamurthy

Meadows

et al. 2020

Human Brain Mapping

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Quantifying accurate functional magnetic resonance imaging (fMRI) activation maps can be dampened by spatio‐temporally varying task‐correlated motion (TCM) artifacts in certain task paradigms (e.g., overt speech). Such real‐world tasks are relevant to characterize longitudinal brain reorganization poststroke, and removal of TCM artifacts is vital for improved clinical interpretation and translation. In this study, we developed a novel independent component analysis (ICA)‐based approach to denoise spatio‐temporally varying TCM artifacts in 14 persons with aphasia who participated in an overt language fMRI paradigm. We compared the new methodology with other existing approaches such as “standard” volume registration, nonselective motion correction ICA packages (i.e., AROMA), and combining the novel approach with AROMA. Results show that the proposed methodology outperforms other approaches in removing TCM‐related false positive activity (i.e., improved detectability power) with high spatial specificity. The proposed method was also effective in maintaining a balance between removal of TCM‐related trial‐by‐trial variability and signal retention. Finally, we show that the TCM artifact is related to clinical metrics, such as speech fluency and aphasia severity, and the implication of TCM denoising on such relationship is also discussed. Overall, our work suggests that routine bulkhead motion based denoising packages cannot effectively account for spatio‐temporally varying TCM. Further, the proposed TCM denoising approach requires a one‐time front‐end effort to hand label and train the classifiers that can be cost‐effectively utilized to denoise large clinical data sets.

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“…Finally, to assess if the BOLD energetics changed as a function of tissue viability, we extracted the unthresholded Z-transformed area under the curve of the HRF (Krishnamurthy et al, 2020(Krishnamurthy et al, , 2021. Briefly, we computed the area under the curve (AUC) of all beta coefficients describing the HRF (12 tent functions that jointly describe the 3dDeconvolve output impulse response function), followed by a Z-transform of the AUC for each participant to normalize the distribution in preparation for parametric statistics.…”

Section: Validation Of Tigr-identified Pericavitational Regions Using Task Fmrimentioning

confidence: 99%

Not All Lesioned Tissue Is Equal: Identifying Pericavitational Areas in Chronic Stroke With Tissue Integrity Gradation via T2w T1w Ratio

Krishnamurthy

Rodriguez

et al. 2021

Front. Neurosci.

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Stroke-related tissue damage within lesioned brain areas is topologically non-uniform and has underlying tissue composition changes that may have important implications for rehabilitation. However, we know of no uniformly accepted, objective non-invasive methodology to identify pericavitational areas within the chronic stroke lesion. To fill this gap, we propose a novel magnetic resonance imaging (MRI) methodology to objectively quantify the lesion core and surrounding pericavitational perimeter, which we call tissue integrity gradation via T2w T1w ratio (TIGR). TIGR uses standard T1-weighted (T1w) and T2-weighted (T2w) anatomical images routinely collected in the clinical setting. TIGR maps are analyzed with relation to subject-specific gray matter and cerebrospinal fluid thresholds and binned to create a false colormap of tissue damage within the stroke lesion, and these are further categorized into low-, medium-, and high-damage areas. We validate TIGR by showing that the cerebral blood flow within the lesion reduces with greater tissue damage (p = 0.005). We further show that a significant task activity can be detected in pericavitational areas and that medium-damage areas contain a significantly lower magnitude of hemodynamic response function than the adjacent damaged areas (p < 0.0001). We also demonstrate the feasibility of using TIGR maps to extract multivariate brain–behavior relationships (p < 0.05) and show general agreement in location compared to binary lesion, T1w-only, and T2w-only maps but that the extent of brain behavior maps may depend on signal sensitivity as denoted by the sparseness coefficient (p < 0.0001). Finally, we show the feasibility of quantifying TIGR in early and late subacute stroke phases, where higher-damage areas were smaller in size (p = 0.002) and that lesioned voxels transition from lower to higher damage with increasing time post-stroke (p = 0.004). We conclude that TIGR is able to (1) identify tissue damage gradient within the stroke lesion across different post-stroke timepoints and (2) more objectively delineate lesion core from pericavitational areas wherein such areas demonstrate reasonable and expected physiological and functional impairments. Importantly, because T1w and T2w scans are routinely collected in the clinic, TIGR maps can be readily incorporated in clinical settings without additional imaging costs or patient burden to facilitate decision processes related to rehabilitation planning.

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Correcting Task fMRI Signals for Variability in Baseline CBF Improves BOLD-Behavior Relationships: A Feasibility Study in an Aging Model

Cited by 13 publications

References 62 publications

The Utility of Cerebrovascular Reactivity MRI in Brain Rehabilitation: A Mechanistic Perspective

The Utility of Cerebrovascular Reactivity MRI in Brain Rehabilitation: A Mechanistic Perspective

A method to mitigate spatio‐temporally varying task‐correlated motion artifacts from overt‐speech fMRI paradigms in aphasia

Not All Lesioned Tissue Is Equal: Identifying Pericavitational Areas in Chronic Stroke With Tissue Integrity Gradation via T2w T1w Ratio

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