Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy.

Deep brain stimulation (DBS) depends on precise delivery of electrical current to target tissues. However, the specific brain structures responsible for best outcome are still debated. We applied probabilistic stimulation mapping to a retrospective, multidisorder DBS dataset assembled over 15 years at our institution (ntotal = 482 patients; nParkinson disease = 303; ndystonia = 64; ntremor = 39; ntreatment-resistant depression/anorexia nervosa = 76) to identify the neuroanatomical substrates of optimal clinical response. Using high-resolution structural magnetic resonance imaging and activation volume modeling, probabilistic stimulation maps (PSMs) that delineated areas of above-mean and below-mean response for each patient cohort were generated and defined in terms of their relationships with surrounding anatomical structures. Our results show that overlap between PSMs and individual patients' activation volumes can serve as a guide to predict clinical outcomes, but that this is not the sole determinant of response. In the future, individualized models that incorporate advancements in mapping techniques with patient-specific clinical variables will likely contribute to the optimization of DBS target selection and improved outcomes for patients. ANN NEUROL 2021;89:426-443.

Ipsilateral and axial tremor response to focused ultrasound thalamotomy for essential tremor: clinical outcomes and probabilistic mapping.

BACKGROUND: MR-guided focused ultrasound (MRgFUS) thalamotomy has been shown to be a safe and effective treatment for essential tremor (ET). OBJECTIVE: To investigate the effects of MRgFUS in patients with ET with an emphasis on ipsilateral-hand and axial tremor subscores. METHODS: Tremor scores and adverse effects of 100 patients treated between 2012 and 2018 were assessed at 1 week, 3, 12, and 24 months. A subgroup analysis of ipsilateral-hand tremor responders (defined as patients with ≥30% improvement at any time point) and non-responders was performed. Correlations and predictive factors for improvement were analysed. Weighted probabilistic maps of improvement were generated. RESULTS: Significant improvement in axial, contralateral-hand and total tremor scores was observed at all study visits from baseline (p<0.0001). There was no significant improvement in ipsilateral subscores. A subset of patients (n=20) exhibited group-level ipsilateral-hand improvement that remained significant through all follow-ups (p<0.001). Multivariate regression analysis revealed that higher baseline scores predict better improvement in ipsilateral-hand and axial tremor. Probabilistic maps demonstrated that the lesion hotspot for axial improvement was situated more medially than that for contralateral improvement. CONCLUSION: MRgFUS significantly improved axial, contralateral-hand and total tremor scores. In a subset of patients, a consistent group-level treatment effect was observed for ipsilateral-hand tremor. While ipsilateral improvement seemed to be less directly related to lesion location, a spatial relationship between lesion location and axial and contralateral improvement was observed that proved consistent with the somatotopic organisation of the ventral intermediate nucleus. TRIAL REGISTRATION NUMBERS: NCT01932463, NCT01827904, and NCT02252380.

Disruption of functional organization within the primary motor cortex in children with autism.

Accumulating evidence suggests that motor impairments are prevalent in autism spectrum disorder (ASD), relate to the social and communicative deficits at the core of the diagnosis and may reflect abnormal connectivity within brain networks underlying motor control and learning. Parcellation of resting-state functional connectivity data using spectral clustering approaches has been shown to be an effective means of visualizing functional organization within the brain but has most commonly been applied to explorations of normal brain function. This article presents a parcellation of a key area of the motor network, the primary motor cortex (M1), a key area of the motor control network, in adults, typically developing (TD) children and children with ASD and introduces methods for selecting the number of parcels, matching parcels across groups and testing group differences. The parcellation is based solely on patterns of connectivity between individual M1 voxels and all voxels outside of M1, and within all groups, a gross dorsomedial to ventrolateral organization emerged within M1 which was left-right symmetric. Although this gross organizational scheme was present in both groups of children, statistically significant group differences in the size and segregation of M1 parcels within regions of the motor homunculus corresponding to the upper and lower limbs were observed. Qualitative comparison of the M1 parcellation for children with ASD with that of younger and older TD children suggests that these organizational differences, with a lack of differentiation between lower limb/trunk regions and upper limb/hand regions, may be due, at least in part, to a delay in functional specialization within the motor cortex.

Optogenetic-guided cortical plasticity after nerve injury.

Peripheral nerve injury causes sensory dysfunctions that are thought to be attributable to changes in neuronal activity occurring in somatosensory cortices both contralateral and ipsilateral to the injury. Recent studies suggest that distorted functional response observed in deprived primary somatosensory cortex (S1) may be the result of an increase in inhibitory interneuron activity and is mediated by the transcallosal pathway. The goal of this study was to develop a strategy to manipulate and control the transcallosal activity to facilitate appropriate plasticity by guiding the cortical reorganization in a rat model of sensory deprivation. Since transcallosal fibers originate mainly from excitatory pyramidal neurons somata situated in laminae III and V, the excitatory neurons in rat S1 were engineered to express halorhodopsin, a light-sensitive chloride pump that triggers neuronal hyperpolarization. Results from electrophysiology, optical imaging, and functional MRI measurements are concordant with that within the deprived S1, activity in response to intact forepaw electrical stimulation was significantly increased by concurrent illumination of halorhodopsin over the healthy S1. Optogenetic manipulations effectively decreased the adverse inhibition of deprived cortex and revealed the major contribution of the transcallosal projections, showing interhemispheric neuroplasticity and thus, setting a foundation to develop improved rehabilitation strategies to restore cortical functions.

A large normative connectome for exploring the tractographic correlates of focal brain interventions.

Diffusion-weighted MRI (dMRI) is a widely used neuroimaging modality that permits the in vivo exploration of white matter connections in the human brain. Normative structural connectomics - the application of large-scale, group-derived dMRI datasets to out-of-sample cohorts - have increasingly been leveraged to study the network correlates of focal brain interventions, insults, and other regions-of-interest (ROIs). Here, we provide a normative, whole-brain connectome in MNI space that enables researchers to interrogate fiber streamlines that are likely perturbed by given ROIs, even in the absence of subject-specific dMRI data. Assembled from multi-shell dMRI data of 985 healthy Human Connectome Project subjects using generalized Q-sampling imaging and multispectral normalization techniques, this connectome comprises ~12 million unique streamlines, the largest to date. It has already been utilized in at least 18 peer-reviewed publications, most frequently in the context of neuromodulatory interventions like deep brain stimulation and focused ultrasound. Now publicly available, this connectome will constitute a useful tool for understanding the wider impact of focal brain perturbations on white matter architecture going forward.

Motor "dexterity"?: Evidence that left hemisphere lateralization of motor circuit connectivity is associated with better motor performance in children.

Motor control relies on well-established motor circuits, which are critical for typical child development. Although many imaging studies have examined task activation during motor performance, none have examined the relationship between functional intrinsic connectivity and motor ability. The current study investigated the relationship between resting state functional connectivity within the motor network and motor performance assessment outside of the scanner in 40 typically developing right-handed children. Better motor performance correlated with greater left-lateralized (mean left hemisphere-mean right hemisphere) motor circuit connectivity. Speed, rhythmicity, and control of movements were associated with connectivity within different individual region pairs: faster speed was associated with more left-lateralized putamen-thalamus connectivity, less overflow with more left-lateralized supplementary motor-primary motor connectivity, and less dysrhythmia with more left-lateralized supplementary motor-anterior cerebellar connectivity. These findings suggest that for right-handed children, superior motor development depends on the establishment of left-hemisphere dominance in intrinsic motor network connectivity.

Atlas-based analysis of resting-state functional connectivity: evaluation for reproducibility and multi-modal anatomy-function correlation studies.

Resting state functional connectivity MRI (rsfc-MRI) reveals a wealth of information about the functional organization of the brain, but poses unique challenges for quantitative image analysis, mostly related to the large number of voxels with low signal-to-noise ratios. In this study, we tested the idea of using a prior spatial parcellation of the entire brain into various structural units, to perform an analysis on a structure-by-structure, rather than voxel-by-voxel, basis. This analysis, based upon atlas parcels, potentially offers enhanced SNR and reproducibility, and can be used as a common anatomical framework for cross-modality and cross-subject quantitative analysis. We used Large Deformation Diffeomorphic Metric Mapping (LDDMM) and a deformable brain atlas to parcel each brain into 185 regions. To investigate the precision of the cross-subject analysis, we computed inter-parcel correlations in 20 participants, each of whom was scanned twice, as well as the consistency of the connectivity patterns inter- and intra-subject, and the intersession reproducibility. We report significant inter-parcel correlations consistent with previous findings, and high test-retest reliability, an important consideration when the goal is to compare clinical populations. As an example of the cross-modality analysis, correlation with anatomical connectivity is also examined.

Dysgeusia induced and resolved by focused ultrasound thalamotomy: case report.

Dysgeusia, or distorted taste, has recently been acknowledged as a complication of thalamic ablation or thalamic deep brain stimulation as a treatment of tremor. In a unique patient, left-sided MR-guided focused ultrasound thalamotomy improved right-sided essential tremor but also induced severe dysgeusia. Although dysgeusia persisted and caused substantial weight loss, tremor slowly relapsed. Therefore, 19 months after the first procedure, the patient underwent a second focused ultrasound thalamotomy procedure, which again improved tremor but also completely resolved the dysgeusia. On the basis of normative and patient-specific whole-brain tractography, the authors determined the relationship between the thalamotomy lesions and the medial border of the medial lemniscus-a surrogate for the solitariothalamic gustatory fibers-after the first and second focused ultrasound thalamotomy procedures. Both tractography methods suggested partial and complete disruption of the solitariothalamic gustatory fibers after the first and second thalamotomy procedures, respectively. The tractography findings in this unique patient demonstrate that incomplete and complete disruption of a neural pathway can induce and resolve symptoms, respectively, and serve as the rationale for ablative procedures for neurological and psychiatric disorders.

On the relationship between seed-based and ICA-based measures of functional connectivity.

Brain functional connectivity (FC) refers to inter-regional synchrony of low frequency fluctuations in blood oxygenation level dependent functional magnetic resonance imaging. FC has been evaluated both during task performance and in the "resting" state, yielding reports of FC differences correlated with behavior and diagnosis. Two methodologies are widely used for evaluating FC from blood oxygenation level dependent functional magnetic resonance imaging data: Temporal correlation with a specified seed voxel or small region of interest; and spatial independent component analysis. While results from seed-based and independent component analysis methodologies are generally similar, they are conceptually different. This study is intended to elucidate and illustrate, qualitatively and quantitatively, the relationship between seed and independent component analysis derived measures of FC. Seed-based FC measures are shown to be the sum of independent component analysis-derived within network connectivities and between network connectivities. We present a simple simulation and an experiment on visuomotor activity that highlight this relationship between the two methods.

Predicting optimal deep brain stimulation parameters for Parkinson's disease using functional MRI and machine learning.

Commonly used for Parkinson's disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the large number of possible stimulation settings (i.e., programming) requires numerous clinic visits. Here, we examine whether functional magnetic resonance imaging (fMRI) can be used to predict optimal stimulation settings for individual patients. We analyze 3 T fMRI data prospectively acquired as part of an observational trial in 67 PD patients using optimal and non-optimal stimulation settings. Clinically optimal stimulation produces a characteristic fMRI brain response pattern marked by preferential engagement of the motor circuit. Then, we build a machine learning model predicting optimal vs. non-optimal settings using the fMRI patterns of 39 PD patients with a priori clinically optimized DBS (88% accuracy). The model predicts optimal stimulation settings in unseen datasets: a priori clinically optimized and stimulation-naïve PD patients. We propose that fMRI brain responses to DBS stimulation in PD patients could represent an objective biomarker of clinical response. Upon further validation with additional studies, these findings may open the door to functional imaging-assisted DBS programming.

Mapping the network underpinnings of central poststroke pain and analgesic neuromodulation.

Central poststroke pain (CPSP) is a debilitating and often treatment-refractory condition that affects numerous stroke patients. The location of lesions most likely to cause pain and the identity of the functional brain networks that they impinge upon remain incompletely understood. We aimed to (1) elucidate which lesion locations are most frequently accompanied by pain; (2) explore CPSP-associated functional networks; and (3) examine how neuromodulation interacts with these networks. This multisite study investigated 17 CPSP patients who received deep brain stimulation (DBS; n = 12) or motor cortex stimulation (MCS; n = 5). Pain-causing lesions were manually segmented and normalized to standard space. To identify areas linked to high risk of pain, the locations of CPSP lesions and 220 control lesions were compared using voxelwise odds ratio mapping. The functional connectivity of pain-causing lesions was obtained using a large (n = 1000) normative resting-state functional MRI connectome and compared to that of control lesions and therapeutic DBS activation volumes. Brain regions most associated with CPSP risk (highest value = 63 times) were located along the ascending somatosensory pathways. These areas and the majority of individual CPSP lesions were functionally connected to anterior/middle cingulate cortex, insula, thalamus, and inferior parietal lobule (PBonferroni < 0.05). The extent of connectivity to the thalamus, inferior parietal lobule, and precuneus also differed between CPSP and control lesions (PBonferroni < 0.05). Posterior insula and thalamus shared connectivity with both CPSP lesions and pain-alleviating DBS activation volumes (PBonferroni < 0.05). These findings further clarify the topography and functional connectivity of pain-causing brain lesions, and provide new insights into the network-level mechanism of CPSP neuromodulation.

Probing the circuitry of panic with deep brain stimulation: Connectomic analysis and review of the literature.

BACKGROUND: Panic attacks affect a sizeable proportion of the population. The neurocircuitry of panic remains incompletely understood. OBJECTIVE: To investigate the neuroanatomical underpinnings of panic attacks induced by deep brain stimulation (DBS) through (1) connectomic analysis of an obsessive-compulsive disorder patient who experienced panic attacks during inferior thalamic peduncle DBS; (2) appraisal of existing clinical reports on DBS-induced panic attacks. METHODS: Panicogenic, ventral contact stimulation was compared with benign stimulation at other contacts using volume of tissue activated (VTA) modelling. Networks associated with the panicogenic zone were investigated using state-of-the-art normative connectivity mapping. In addition, a literature search for prior reports of DBS-induced panic attacks was conducted. RESULTS: Panicogenic VTAs impinged primarily on the tuberal hypothalamus. Compared to non-panicogenic VTAs, panicogenic loci were significantly functionally coupled to limbic and brainstem structures, including periaqueductal grey and amygdala. Previous studies found stimulation of these areas can also provoke panic attacks. CONCLUSIONS: DBS in the region of the tuberal hypothalamus elicited panic attacks in a single obsessive-compulsive disorder patient and recruited a network of structures previously implicated in panic pathophysiology, reinforcing the importance of the hypothalamus as a hub of panicogenic circuitry.

Longitudinal Resting State Functional Connectivity Predicts Clinical Outcome in Mild Traumatic Brain Injury.

Mild traumatic brain injury (mTBI) affects about 42 million people worldwide. It is often associated with headache, cognitive deficits, and balance difficulties but rarely shows any abnormalities on conventional computed tomography (CT) or magnetic resonance imaging (MRI). Although in most mTBI patients the symptoms resolve within 3 months, 10-15% of patients continue to exhibit symptoms beyond a year. Also, it is known that there exists a vulnerable period post-injury, when a second injury may exacerbate clinical prognosis. Identifying this vulnerable period may be critical for patient outcome, but very little is known about the neural underpinnings of mTBI and its recovery. In this work, we used advanced functional neuroimaging to study longitudinal changes in functional organization of the brain during the 3-month recovery period post-mTBI. Fractional amplitude of low frequency fluctuations (fALFF) measured from resting state functional MRI (rs-fMRI) was found to be associated with symptom severity score (SSS, r = -0.28, p = 0.002). Decreased fALFF was observed in specific functional networks for patients with higher SSS, and fALFF returned to higher values when the patient recovered (lower SSS). In addition, functional connectivity of the same networks was found to be associated with concurrent SSS, and connectivity immediately after injury (<10 days) was capable of predicting SSS at a later time-point (3 weeks to 3 months, p < 0.05). Specific networks including motor, default-mode, and visual networks were found to be associated with SSS (p < 0.001), and connectivity between these networks predicted 3-month clinical outcome (motor and visual: p < 0.001, default-mode: p < 0.006). Our results suggest that functional connectivity in these networks comprise potential biomarkers for predicting mTBI recovery profiles and clinical outcome.

Multiband fMRI as a plausible, time-saving technique for resting-state data acquisition: Study on functional connectivity mapping using graph theoretical measures.

BACKGROUND AND OBJECTIVES: Ensuring patient comfort and compliance by emphasizing reduced time frame for image acquisition, without compromising image quality is the key aspect with functional MRI examination. Multiband resting state fMRI (MB-rsfMRI) is a fairly new technique that potentially shortens MR image acquisition time by providing increased number of time points. The study aims to compare signal characteristics as well as the functional connectivity using conventional resting-state fMRI (rsfMRI) with that of MB-rsfMRI technique. MATERIALS AND METHODS: 9 healthy volunteers have prospectively undergone conventional resting-state fMRI and Multiband rsfMRI scanning technique in a 3T GE scanner (Discovery MR750w™). We compared the temporal SNR (tSNR) of conventional rs-fMRI with that of MB-rsfMRI. We looked at the language network connectivity and small world network characteristics from graph theoretical measures to compare the two techniques. RESULTS: We computed the tSNR of conventional resting-state fMRI (rsfMRI) and MB-rsfMRI technique. A strong positive correlation was seen between graph theoretical measures from MB-rsfMRI and conventional rsfMRI (Pearson Correlation, r = 0.99). Both techniques showed similar small world network characteristics in healthy controls. CONCLUSION: The present study demonstrates negligible differences between the conventional-rsfMRI and MB-rsfMRI acquisitions on the computed graph theoretic measures. Accordingly current analysis proves that MB-rs-fMRI may be used as a time reducing acquisition technique that enables mapping of functional connectivity with similar outcome as conventional rs-fMRI in healthy subjects.

Recursive feature elimination for biomarker discovery in resting-state functional connectivity.

Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years.

Resting-state functional MRI (rs-fMRI) permits study of the brain's functional networks without requiring participants to perform tasks. Robust changes in such resting state networks (RSNs) have been observed in neurologic disorders, and rs-fMRI outcome measures are candidate biomarkers for monitoring clinical trials, including trials of extended therapeutic interventions for rehabilitation of patients with chronic conditions. In this study, we aim to present a unique longitudinal dataset reporting on a healthy adult subject scanned weekly over 3.5 years and identify rs-fMRI outcome measures appropriate for clinical trials. Accordingly, we assessed the reproducibility, and characterized the temporal structure of, rs-fMRI outcome measures derived using independent component analysis (ICA). Data was compared to a 21-person dataset acquired on the same scanner in order to confirm that the values of the single-subject RSN measures were within the expected range as assessed from the multi-participant dataset. Fourteen RSNs were identified, and the inter-session reproducibility of outcome measures-network spatial map, temporal signal fluctuation magnitude, and between-network connectivity (BNC)-was high, with executive RSNs showing the highest reproducibility. Analysis of the weekly outcome measures also showed that many rs-fMRI outcome measures had a significant linear trend, annual periodicity, and persistence. Such temporal structure was most prominent in spatial map similarity, and least prominent in BNC. High reproducibility supports the candidacy of rs-fMRI outcome measures as biomarkers, but the presence of significant temporal structure needs to be taken into account when such outcome measures are considered as biomarkers for rehabilitation-style therapeutic interventions in chronic conditions.

Impaired cortico-striatal functional connectivity in prodromal Huntington's Disease.

Huntington's Disease (HD) is a neurodegenerative disease caused by a CAG triplet-repeat expansion-mutation in the Huntingtin gene. Subjects at risk for HD can be identified by genetic testing in the prodromal phase. Structural changes of basal-ganglia nuclei such as the caudate nucleus are well-replicated findings observable early in prodromal-HD subjects and may be preceded by distinct functional alterations of cortico-striatal circuits. This study aims to assess functional integrity of the motor system as a cortico-striatal circuit with particular clinical relevance in HD. Ten subjects in the prodromal phase of HD and ten matched controls were administered blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) at rest (3T). Functional connectivity was measured as synchrony of BOLD activity between the caudate nucleus and thirteen cortical brain regions (seeds). Basal-ganglia volumes were assessed as established markers of disease progression in prodromal-HD. Linear regression analysis was performed to test for a relationship between structural changes and group differences in functional connectivity. Prodromal-HD subjects showed reduced BOLD synchrony between two seeds in the premotor cortex (BA6) and the caudate nucleus. While similar effect sizes could be observed for reduced basal-ganglia volumes and differences in functional connectivity, coefficients of determination indicate a moderate relationship between functional connectivity and striatal atrophy. Our data show reduced cortico-striatal functional connectivity at rest in prodromal-HD and suggest a relation to early structural brain changes. Additional longitudinal studies are necessary to elucidate the temporal relationship between functional alterations and earliest structural brain changes in prodromal-HD.

Depressive symptoms in prodromal Huntington's Disease correlate with Stroop-interference related functional connectivity in the ventromedial prefrontal cortex.

Huntington's Disease (HD) is a neurodegenerative disorder caused by a cytosine-adenine-guanine (CAG) triplet repeat-expansion in the Huntingtin (HTT) gene. Diagnosis of HD is classically defined by the presence of motor symptoms; however, cognitive and depressive symptoms frequently precede motor manifestations, and may occur early in the prodromal phase. There are sparse data so far on functional brain correlates of depressive symptoms in prodromal HD. A Stroop color-naming test was administered to 32 subjects in the prodromal phase of HD and 52 expansion-negative controls while performing functional magnetic resonance imaging at 3Tesla. Networks of functional connectivity were identified using group independent component analysis, followed by an analysis of functional network interactions. A contrast of temporal regression-based beta-weights was calculated as a reflection of Stroop-interference related activity and correlated with Center for Epidemiologic Studies Depression (CES-D) scores. For secondary analysis, patients were stratified into two subgroups by median split of CAG repeat-length. Stroop performance was independent of HTT mutation-carrier status and CES-D score. Stroop-interference-related activity of the ventromedial prefrontal cortex-node of the default-mode network, calculated by temporal-regression beta-weights, was more highly correlated with depressive symptoms in subjects in the prodromal phase of HD than in controls, differing significantly. The strength of this correlation and its difference from controls increased when a subgroup of patients with longer CAG repeat lengths was analyzed. These findings suggest that depressive symptoms in prodromal HD subjects may reflect altered functional brain network activity in the context of early HD-related brain alterations.

Assessing quality of hybridized RNA in Affymetrix GeneChip experiments using mixed-effects models.

The technology for hybridizing archived tissue specimens and the use of laser-capture microdissection for selecting cell populations for RNA extraction have increased over the past few years. Both these methods contribute to RNA degradation. Therefore, quality assessments of RNA hybridized to microarrays are becoming increasingly more important. Existing methods for estimating the quality of RNA hybridized to a GeneChip, from resulting microarray data, suffer from subjectivity and lack of estimates of variability. In this article, a method for assessing RNA quality for a hybridized array which overcomes these drawbacks is proposed. The effectiveness of the proposed method is demonstrated by the application of the method to two microarray data sets for which external verification of RNA quality is known.

Discovery of gradient pattern in dominant frequency maps during fibrillation: implication of rotor drift and epicardial conduction velocity changes.

Dominant frequency (DF) maps for mapping epicardial activations of ventricular fibrillation (VF) have been studied mainly using fast Fourier transform (FFT). Small and discrete DF domains exhibited in these DF maps have undermined the hypothesis of mother rotor for VF maintenance. We applied continuous Fourier transform (CFT) to generate high-precision DF maps and studied characteristics of these high-precision DF maps. Optical epicardial activations were recorded in isolated rabbit hearts (n=10). Continuous Fourier transform of 1-second segments was performed in VF (n=188) and ventricular tachycardia (n=189) at 0.1 Hz precisions. Banded gradient patterns of gradual change in DF values were observed in 136 of 188 VF segments, but not in ventricular tachycardia. These gradients were not observed when FFT was used. Gradients were observed along the conduction path of reentrant-like waves with decreasing DF values along the path. Spectra in the gradients did not exhibit bimodal spectra as is usually observed in traditional DF domain boundaries. Time-space plots revealed clear association between gradient pattern and epicardial conduction velocity changes. Prior simulation studies predicted a gradient in activation rate during rotor drift. This gradient pattern has been observed for the first time experimentally by only using CFT, but not FFT. High-precision DF videos indicated the existence of gradient movement from one spatial location to another, smoothly instead of randomly disappearing from one location and appearing in another. The discovery of associated pseudoconduction velocity changes, and gradient patterns might suggest that dominant rotor (mother rotor) drifting plays a maintenance role only detectable by CFT and not FFT.