Hemodynamically informed parcellation of cerebral FMRI data

Standard detection of evoked brain activity in functional MRI (fMRI) relies on a fixed and known shape of the impulse response of the neurovascular coupling, namely the hemodynamic response function (HRF). To cope with this issue, the joint detection-estimation (JDE) framework has been proposed. This formalism enables to estimate a HRF per region but for doing so, it assumes a prior brain partition (or parcellation) regarding hemodynamic territories. This partition has to be accurate enough to recover accurate HRF shapes but has also to overcome the detection-estimation issue: the lack of hemodynamics information in the non-active positions. An hemodynamically-based parcellation method is proposed, consisting first of a feature extraction step, followed by a Gaussian Mixture-based parcellation, which considers the injection of the activation levels in the parcellation process, in order to overcome the detection-estimation issue and find the underlying hemodynamics

Combining task-evoked and spontaneous activity to improve pre-operative brain mapping with fMRI

Noninvasive localization of brain function is used to understand and treat neurological disease, exemplified by pre-operative fMRI mapping prior to neurosurgical intervention. The principal approach for generating these maps relies on brain responses evoked by a task and, despite known limitations, has dominated clinical practice for over 20years. Recently, pre-operative fMRI mapping based on correlations in spontaneous brain activity has been demonstrated, however this approach has its own limitations and has not seen widespread clinical use. Here we show that spontaneous and task-based mapping can be performed together using the same pre-operative fMRI data, provide complimentary information relevant for functional localization, and can be combined to improve identification of eloquent motor cortex. Accuracy, sensitivity, and specificity of our approach are quantified through comparison with electrical cortical stimulation mapping in eight patients with intractable epilepsy. Broad applicability and reproducibility of our approach are demonstrated through prospective replication in an independent dataset of six patients from a different center. In both cohorts and every individual patient, we see a significant improvement in signal to noise and mapping accuracy independent of threshold, quantified using receiver operating characteristic curves. Collectively, our results suggest that modifying the processing of fMRI data to incorporate both task-based and spontaneous activity significantly improves functional localization in pre-operative patients. Because this method requires no additional scan time or modification to conventional pre-operative data acquisition protocols it could have widespread utility

Understanding Stroke in the Connected Human Brain

Although structural damage from stroke is focal, remote dysfunction can occur in regions of the brain distant from the area of damage. Lesions in both gray and white matter can disrupt the flow of information in areas connected to or by the area of infarct. This is because the brain is not an assortment of specialized parts but an assembly of distributed networks that interact to support cognitive function. Functional connectivity analyses using resting functional magnetic resonance imaging (fMRI) have shown us that the cortex is organized into distributed brain networks. The primary goal of this work is to characterize the effects of stroke on distributed brain systems and to use this information to better understand neural correlates of deficit and recovery following stroke. We measured resting functional connectivity, lesion topography, and behavior in multiple domains (attention, visual memory, verbal memory, language, motor, and visual) in a cohort of 132 stroke patients. Patients were followed longitudinally with full behavioral and imaging batteries acquired at 2 weeks, 3 months, and 1 year post-stroke. Thirty age- and demographic- matched controls were scanned twice at an interval of three months. In chapter 1, we explore a central question motivating this work: how is behavior represented in the brain? We review progressing prospective – from basic functional localization to newer theories connecting inter-related brain networks to cognitive operations. In so doing, we attempt to build a foundation that motivates the hypotheses and experimental approaches explored in this work. Chapters 2 and 3 serve primarily to validate approaches and considerations for using resting fMRI to measure functional connectivity in stroke patients. In chapter 2, we investigate hemodynamic lags after stroke. ‘Hemodynamic lag’ is a local delay in the blood oxygen level dependent (BOLD) response to neural activity, measured using cross-correlation of local fMRI signal with some reference brain signal. This work tests assumptions of the BOLD response to neural activity after stroke, but also provides novel and clinically relevant insight into perilesional disruption to hemodynamics. Significant lags are observed in 30% of stroke patients sub-acutely and 10% of patients at one-year. Hemodynamic lag corresponds to gross aberrancy in functional connectivity measures, performance deficits and local and global perfusion deficits. Yet, relationships between functional connectivity and behavior reviewed in chapter 1 persist after hemodynamic delays is corrected for. Chapter 3 provides a more extended discussion of approaches and considerations for using resting fMRI to measure functional connectivity in stroke patients. Like chapter 1, the goal is to motivate experimental approaches taken in later chapters. But here, more technical challenges relating to brain co-registration, neurovascular coupling, and clinical population selection are considered. In chapter 4, we uncover the relationships between local damage, network wide functional disconnection, and neurological deficit. We find that visual memory and verbal memory are better predicted by connectivity, whereas visual and motor deficits are better predicted by lesion topography. Attention and language deficits are well predicted by both. We identify a general pattern of physiological network dysfunction consisting of decrease of inter-hemispheric integration and decrease in intra-hemispheric segregation, which strongly related to behavioral impairment in multiple domains. In chapter 5, we explore a case study of abulia – severe apathy. This work ties together principles of local damage, network disruption, and network-related deficit and demonstrates how they can be useful in understanding and developing targeted treatments (such as transcranial magnetic stimulation) for individual stroke patients. In chapter 6, we explore longitudinal changes in functional connectivity that parallel recovery. We find that the topology and boundaries of cortical regions remains unchanged across recovery, empirically validating our parcel-wise connectivity approach. In contrast, we find that the modularity of brain systems i.e. the degree of integration within and segregation between networks, is significantly reduced after a stroke, but partially recovered over time. Importantly, the return of modular network structure parallels recovery of language and attention, but not motor function. This work establishes the importance of normalization of large-scale modular brain systems in stroke recovery. In chapter 7, we discuss some fundamental revisions of past lesion-deficit frameworks necessitated by recent findings. Firstly, anatomical priors of structural and functional connections are needed to explain why certain lesions across distant locations should share behavioral consequences. Secondly, functional priors of connectomics are needed to explain how local injury can produce widespread disruption to brain connectivity and behavior that have been observed

Subject-level Joint Parcellation-Detection-Estimation in fMRI

Brain parcellation is one of the most important issues in functional MRI (fMRI) data analysis. This parcellation allows establishing homogeneous territories that share the same functional properties. This paper presents a model-based approach to perform a subject-level parcellation into hemodynamic territories with similar hemodynamic features which are known to vary between brain regions. We specifically investigate the use of the Joint Parcellation-Detection-Estimation (JPDE) model initially proposed in [1] to separate brain regions that match different hemodynamic response function (HRF) profiles. A hierarchical Bayesian model is built and a variational expectation maximiza-tion (VEM) algorithm is deployed to perform inference. A more complete version of the JPDE model is detailed. Validation on synthetic data shows the robustness of this model to varying signal-to-noise ratio (SNR) as well as to different initializations. Our results also demonstrate that good parcellation performance is achieved even though the parcels do not involve the same amount of activation. On real fMRI data acquired in children during a language paradigm, we retrieved a parcellation along the superior temporal sulcus of the left hemisphere that matches the gradient of activation dynamics already reported in the literature

Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework

Submitted to IEEE Transactions on Medical ImagingIdentifying brain hemodynamics in event-related functional MRI (fMRI) data is a crucial issue to disentangle the vascular response from the neuronal activity in the BOLD signal. This question is usually addressed by estimating the so-called Hemodynamic Response Function (HRF). Voxelwise or region-/parcelwise inference schemes have been proposed to achieve this goal but so far all known contributions commit to pre-specified spatial supports for the hemodynamic territories by defining these supports either as individual voxels or a priori fixed brain parcels. In this paper, we introduce a Joint Parcellation-Detection-Estimation (JPDE) procedure that incorporates an adaptive parcel identification step based upon local hemodynamic properties. Efficient inference of both evoked activity, HRF shapes and supports is then achieved using variational approximations. Validation on synthetic and real fMRI data demonstrates the JPDE performance over standard detection estimation schemes and suggests it as a new brain exploration tool

Bayesian joint detection-estimation in functional MRI with automatic parcellation and functional constraints

Brain parcellation into a number of hemodynamically homogeneous regions (parcels) is a challenging issue in fMRI analyses. An automatic inference for the parcels from the fMRI data was proposed in the framework of the joint parcellation detection estimation (JPDE) model. However, this model still requires appropriate prior information about the number of parcels and their shapes provided through an initial parcellation, which is a challenging task since it generally depends on the subject. In this thesis, we present novel approaches for hemodynamic brain parcellation. These approaches are motivated by the fact that the hemodynamic response function varies across brain regions and sessions within subjects, and even among subjects and groups. The proposed approaches belong to one of two main categories, the subject-level and group-level fMRI data analysis models. For the subjectlevel fMRI data analysis, we propose three models to automatically estimate the optimum number of parcels and their shapes directly from fMRI data. The first one is formulated as a model selection procedure added to the framework of the classical JPDE model in which we compute the free energy for the candidate models, each with different number of parcels, and then select the one that maximizes this energy. To overcome the computational intensity associated with the first approach, we propose a second method which relies on a Bayesian non-parametric model where a combination of a Dirichlet process mixture model and a hidden Markov random field is used to allow for unlimited number of parcels and then estimate the optimal one. Finally to avoid the computational complexity associated with the estimation of the interaction parameter of the Markov field in the second approach, we make use of a well known clustering algorithm (the mean shift) and embed it in the framework of the JPDE model to automatically infer the number of parcels by estimating the modes of the underlying multivariate distribution. All the proposed subject-level approaches are validated using synthetic and real data. The obtained results are consistent across approaches in terms of the detection of the elicited activity. Moreover, the second and the third approaches manage to discriminate the hemodynamic response function profiles according to different criteria such as the full width at half maximum and the time to peak. Regarding the group-level fMRI analysis, we propose two new models that are able to estimate group-level parcellation and hemodynamic response function profiles. The JPDE model is extended to allow for this group-level estimation by considering data coming from all the subjects resulting in a multisubject joint parcellation detection estimation model. However, in real data experiment, it is noticed that the smoothness of the estimated HRFs is sensitive to one of the hyperparameters. Hence, we resort to the second model that performs inter and intra subject analysis providing estimation at both the single and group-levels. A thorough comparison is conducted between the two models at the group-level where the results are coherent. At the subject-level, a comparison is conducted between the proposed inter and intra subject analysis model and the JPDE one. This comparison indicates that the HRF estimates using our proposed model are more accurate as they are closer to the canonical HRF shape in the right motor cortex. Finally, the estimation of the unknown variables, the parameters and the hyperparameters in all of the proposed approaches is addressed from a Bayesian point of view using a variational expectation maximization strategy

Kuuloaivokuoren toiminnallinen organisaatio aktiivisten kuuntelutehtävien aikana

Previous imaging studies have shown that activation in human auditory cortex (AC) is strongly modulated during active listening tasks. However, the prevalent models of AC mainly focus on the processing of stimulus-specific information and speech and do not predict such task-dependent modulation. In the present thesis, functional magnetic resonance imaging was used to measure regional activation in AC during discrimination and n-back memory tasks in order to investigate the relationship between stimulus-specific and task-dependent processing (Study I) and inter-regional connectivity during rest and active tasks (Study III). In addition, source analysis of scalp-recorded event-related potentials was carried out to study the temporal dynamics of task-dependent activation in AC (Study II). In Study I, distinct stimulus-specific activation patterns to pitch-varying and location-varying sounds were similarly observed during visual (no directed auditory attention) and auditory tasks. This is consistent with the prevalent models which presume parallel and independent “what” (e.g. pitch) and “where” processing streams. As expected, discrimination and n-back memory tasks were associated with distinct task-dependent activation patterns. These activation patterns were independent of whether subjects performed pitch or location versions of these tasks. Thus, AC activation during discrimination and n-back memory tasks cannot be explained by enhanced stimulus-specific processing (of pitch and location). Consistently, Study II showed that the task-dependent effects in AC occur relatively late (200–700 ms from stimulus onset) compared to the latency of stimulus-specific pitch processing (0–200 ms). In Study III, the organization of human AC was investigated based on functional connectivity. Connectivity-based parcellation revealed a network structure that consisted of six modules in supratemporal plane, temporal lobe, and inferior parietal lobule in both hemispheres. Multivariate pattern analysis showed that connectivity within this network structure was significantly modulated during the presentation of sounds (visual task) and auditory task performance. Together the results of this thesis show that (1) activation in human AC strongly depends on the requirements of the listening task and that task-dependent modulation is not due to enhanced stimulus-specific processing, (2) regions in inferior parietal lobule play an important role in the processing of both task-irrelevant and task-relevant auditory information in human AC, and (3) the activation patterns in human AC during the presentation of task-irrelevant and task-relevant sounds cannot be fully explained by a hierarchical model in which information is processed in two parallel processing streams.Aiemmat kuvantamistutkimukset ovat osoittaneet, että aktiiviset kuuntelutehtävät vaikuttavat voimakkaasti ihmisen kuuloaivokuoren aktivaatioon. Kuuloaivokuoren toiminnalliset mallit kuitenkin keskittyvät äänten akustisten piirteiden ja puheen käsittelyyn, eivätkä ne siten ennusta tehtäväsidonnaisia vaikutuksia. Tässä väitöskirjassa tutkittiin kuuloaivokuoren toimintaa äänten erottelu- ja n-back-muistitehtävien aikana toiminnallisella magneettikuvauksella ja herätevasterekisteröinnillä. Tutkimusten tavoitteena oli selvittää riippuvatko ärsyke- ja tehtäväsidonnaiset aktivaatiot toisistaan (Tutkimus I) sekä tutkia kuuloaivokuoren eri alueiden välistä toiminnallista konnektiivisuutta lepo- ja tehtävätilanteiden aikana (Tutkimus III). Pään pinnalta mitattujen herätevasteiden lähdemallinnuksen avulla tutkittiin kuuloaivokuoren tehtäväsidonnaisen aktivaation ajallista dynamiikkaa (Tutkimus II). Tutkimuksessa I äänen korkeuden ja tulosuunnan vaihtelu aktivoivat erillisiä kuuloaivokuoren alueita sekä näkötehtävän (ei suunnattua kuulotarkkaavaisuutta) että kuuntelutehtävien aikana. Tämä tulos on yhtenevä vallitsevien kuuloaivokuoren mallien kanssa, joissa oletetaan, että äänen korkeus ja tulosuunta käsitellään rinnakkaisissa ja toisistaan riippumattomissa mitä- (esim. äänen korkeus) ja missä-järjestelmissä. Aktiivisten kuuntelutehtävien aikana kuuloaivokuoren aktivaatiojakauma riippui odotetusti siitä, tekivätkö koehenkilöt äänten erottelu vai n-back-muistitehtävää. Tehtäväsidonnaiset aktivaatiojakaumat (erottelu- ja muistitehtävän erot) olivat kuitenkin hyvin samankaltaisia äänen korkeus- ja tulosuuntatehtävien aikana. Kuuloaivokuoren tehtäväsidonnaisia aktivaatioita äänten erottelu- ja n-back-muistitehtävien aikana ei siten voida selittää ääni-informaation käsittelyyn liittyvien aktivaatioiden voimistumisella. Tätä johtopäätöstä tukevat myös Tutkimuksen II tulokset, joiden mukaan kuuloaivokuoren tehtäväsidonnaiset aktivaatiot (n. 200–700 ms äänen alusta) havaitaan pääosin äänenkorkeustiedon käsittelyyn liittyvän aktivaation (0–200 ms) jälkeen. Tutkimuksessa III selvitettiin kuuloaivokuoren toiminnallista organisaatiota ja sen eri aluiden muodostamia verkostoja konnektiivisuusanalyysien avulla. Näissä analyyseissä havaittiin modulaarinen rakenne, jossa kuuloaivokuori ja sen lähialueiden muodostama verkosto jakaantuu kuuteen osaan (moduuliin). Toiminnallisen konnektiivisuuden muutoksia eri koetilanteissa tarkasteltiin monimuuttujakuvioanalyysillä. Tulokset osoittivat, että konnektiivisuus kuuloaivokuoren ja sen lähialueiden muodostamassa verkostossa muuttui merkitsevästi verrattuna lepotilanteeseen, kun koehenkilöille esitettiin ääniä (näkötehtävän aikana) ja kun he tekivät kuuntelutehtäviä. Väitöskirjan tutkimusten tulokset osoittavat, että (1) ihmisen kuuloaivokuoren aktivaatio riippuu voimakkaasti kuuntelutehtävän vaatimuksista, (2) päälaenlohkon alaosat osallistuvat merkittävällä tavalla kuuloaivokuoren toimintaan ääni-informaation käsittelyn aikana riippumatta siitä, ovatko äänet olennaisia vai epäolennaisia kulloisenkin tehtävän kannalta, ja (3) ihmisen kuuloaivokuoren aktivaatiota ei voida täysin selittää hierarkisella mallilla, jossa ääni-informaatiota käsitellään kahdella rinnakkaisella tiedonkäsittelyradalla

Application of resting-state fMRI methods to acute ischemic stroke

Diffusion weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) perfusion-weighted imaging (PWI) are commonly employed in clinical practice and in research to give pathophysiological information for patients with acute ischemic stroke. DWI is thought to roughly reflect the severely damaged infarct core, while DSC-PWI reflects the area of hypoperfusion. The volumetric difference between DWI and DSC-PWI is termed the PWI/DWI-mismatch, and has been suggested as an MRI surrogate of the ischemic penumbra. However, due to the application of a contrast agent, which has potentially severe side-effects (e.g., nephrogenic systemic fibrosis), the DSC-PWI precludes repetitive examinations for monitoring purposes. New approaches are being sought to overcome this shortcoming. BOLD (blood oxygen-level dependent) signal can reflect the metabolism of blood oxygen in the brain and hemodynamics can be assessed with resting-state fMRI. The aim of this thesis was to use resting-state fMRI as a new approach to give similar information as DSC-PWI. This thesis comprises two studies: In the first study (see Chapter 2), two resting-state fMRI methods, local methods which compare low frequency amplitudes between two hemispheres and a k-means clustering approach, were applied to investigate the functional damage of patients with acute ischemic stroke both in the time domain and frequency domain. We found that the lesion areas had lower amplitudes than contralateral homotopic healthy tissues. We also differentiated the lesion areas from healthy tissues using a k-means clustering approach. In the second study (see Chapter 3), time-shift analysis (TSA), which assesses time delays of the spontaneous low frequency fluctuations of the resting-state BOLD signal, was applied to give similar pathophysiological information as DSC-PWI in the acute phase of stroke. We found that areas which showed a pronounced time delay to the respective mean time course were very similar to the hypoperfusion area. In summary, we suggest that the resting-state fMRI methods, especially the time-shift analysis (TSA), may provide comparable information to DSC-PWI and thus serve as a useful diagnostic tool for stroke MRI without the need for the application of a contrast agent

Transient and Persistent Pain Induced Connectivity Alterations in Pediatric Complex Regional Pain Syndrome

Evaluation of pain-induced changes in functional connectivity was performed in pediatric complex regional pain syndrome (CRPS) patients. High field functional magnetic resonance imaging was done in the symptomatic painful state and at follow up in the asymptomatic pain free/recovered state. Two types of connectivity alterations were defined: (1) Transient increases in functional connectivity that identified regions with increased cold-induced functional connectivity in the affected limb vs. unaffected limb in the CRPS state, but with normalized connectivity patterns in the recovered state; and (2) Persistent increases in functional connectivity that identified regions with increased cold-induced functional connectivity in the affected limb as compared to the unaffected limb that persisted also in the recovered state (recovered affected limb versus recovered unaffected limb). The data support the notion that even after symptomatic recovery, alterations in brain systems persist, particularly in amygdala and basal ganglia systems. Connectivity analysis may provide a measure of temporal normalization of different circuits/regions when evaluating therapeutic interventions for this condition. The results add emphasis to the importance of early recognition and management in improving outcome of pediatric CRPS