Hierarchical Bayesian aspects of distributed neuromagnetic source models

Magnetoencephalography (MEG) enables noninvasive measurements of cerebral activity with excellent temporal resolution, but localising the neural currents generating the extracranial magnetic fields admits no unique solution. By imposing some mathematical constraints on the currents, reasonable solutions to this electromagnetic inverse problem can be obtained. In this work, we adopt the statistical formulation of the inverse problem in which the constraints are encoded as Bayesian prior probabilities. The prior is combined with a statistical MEG observation model via Bayes' theorem to yield the posterior probability of the unknown parameters, that is the currents, given the MEG data and modeling assumptions. Apart from the currents, the prior probability density may contain further parameters which are subject to uncertainty. These parameters are not related directly to the MEG observations and are called second-level parameters or hyperparameters, giving the model a hierarchical structure. The thesis considers hierarchical generalisations of the classical Minimum-Norm and Minimum-Current Estimates (MNE and MCE). The MNE and MCE are distributed source reconstruction methods from which the former is known to produce spatially diffuse distributions and the latter more focal. The here studied extensions of the MNE and MCE prior structures allow more general and flexible modeling of distributed sources with properties in between MNE and MCE. The first two studies included in this thesis involve more theoretical Bayesian analyses on the properties of the hierarchical distributed source models and the resulting inverse estimates. The latter two studies focus on validation of the models with empirical MEG data, practical analyses and interpretation of the inverse estimates.Magnetoenkefalografia (MEG) mahdollistaa pään ulkopuolelta tapahtuvan aivotoimintojen mittaamisen hyvällä ajallisella tarkkuudella, mutta nämä magneettikentät synnyttävien aivokudoksen sähkövirtojen paikallistaminen vaatii ns. sähkömagneettisen käänteisongelman ratkaisun, joka ei ole yksikäsitteinen. Jos virtakonfiguraatioille asetetaan sopivia matemaattisia rajoitteita, on kuitenkin mahdollista löytää käyttökelpoisia ratkaisuja tähän käänteisongelmaan. Tässä työssä käänteisongelmaa lähestytään tilastollisesti, ja matemaattiset rajoitteet muotoillaan Bayesilaisittain a priori todennäköisyyksinä. Tämä priorijakauma yhdistetään tilastollisen MEG-havaintomallin kanssa, jolloin saadaan Bayesin teoreeman avulla tuntemattomien parametrien eli virtakonfiguraatioiden a posteriori -jakauma, joka kertoo eri virtakonfiguraatioden todennäköisyydet, annettuna havaittu data sekä tehdyt mallioletukset. Virtojen lisäksi priorijakaumaan saattaa liittyä muita tuntemattomia suureita, jotka sisältävät epävarmuutta. Nämä parametrit eivät kytkeydy suoraan MEG-mittauksiin, joten ne ovat siis sähkövirtoihin verrattuna seuraavalla mallitasolla. Näitä priorin parametreja kutsutaan hyperparametreiksi, ja mallilla on hierarkinen rakenne. Väitöskirjassa tutkitaan klassisten miniminormi- ja minimivirtaestimaattien hierarkisia yleistyksiä. Miniminormi- ja minimivirtaestimaatit ovat lähdejakaumamalleihin liittyviä menetelmiä, joista ensimmäinen tuottaa paikallisesti varsin laajalle levineitä ja jälkimmäinen fokaalimpia käänteisongelman ratkaisuja. Näiden menetelmien tässä työssä tutkitut laajennukset mahdollistavat myös yleisempien ja joustavampien, ominaisuuksiltaan miniminormi- ja minimivirtaoletusten väliin sijoittuvien lähdejakaumien mallintamisen. Kaksi ensimmäistä osatyötä keskittyvät esitettyjen hierarkisten Bayesilaisten lähdejakaumamallien sekä niiden tuottamien käänteisongelman ratkaisujen teoreettiseen tutkimiseen. Kahdessa jälkimmäisessä osatyössä pyritään validoimaan menetelmät käyttäen mitattua MEG dataa, sekä selventämään näiden hierarkisten käänteisongelman ratkaisujen käytännön merkitystä ja tulkintaa.reviewe

Evidence for distinct human auditory cortex regions for sound location versus identity processing

Neurophysiological animal models suggest that anterior auditory cortex (AC) areas process sound-identity information, whereas posterior ACs specialize in sound location processing. In humans, inconsistent neuroimaging results and insufficient causal evidence have challenged the existence of such parallel AC organization. Here we transiently inhibit bilateral anterior or posterior AC areas using MRI-guided paired-pulse transcranial magnetic stimulation (TMS) while subjects listen to Reference/Probe sound pairs and perform either sound location or identity discrimination tasks. The targeting of TMS pulses, delivered 55–145 ms after Probes, is confirmed with individual-level cortical electric-field estimates. Our data show that TMS to posterior AC regions delays reaction times (RT) significantly more during sound location than identity discrimination, whereas TMS to anterior AC regions delays RTs significantly more during sound identity than location discrimination. This double dissociation provides direct causal support for parallel processing of sound identity features in anterior AC and sound location in posterior AC

A comprehensive diffusion MRI dataset acquired on the MGH Connectome scanner in a biomimetic brain phantom

We provide a comprehensive diffusion MRI dataset acquired with a novel biomimetic phantom mimicking human white matter. The fiber substrates in the diffusion phantom were constructed from hollow textile axons (“taxons”) with an inner diameter of 11.8±1.2 µm and outer diameter of 33.5±2.3 µm. Data were acquired on the 3 T CONNECTOM MRI scanner with multiple diffusion times and multiple q-values per diffusion time, which is a dedicated acquisition for validation of microstructural imaging methods, such as compartment size and volume fraction mapping. Minimal preprocessing was performed to correct for susceptibility and eddy current distortions. Data were deposited in the XNAT Central database (project ID: dMRI_Phant_MGH)

Variability and anatomical specificity of the orbitofrontothalamic fibers of passage in the ventral capsule/ventral striatum (VC/VS): precision care for patient-specific tractography-guided targeting of deep brain stimulation (DBS) in obsessive compulsive disorder (OCD)

Deep Brain Stimulation (DBS) is a neurosurgical procedure that can reduce symptoms in medically intractable obsessive-compulsive disorder (OCD). Conceptually, DBS of the ventral capsule/ventral striatum (VC/VS) region targets reciprocal excitatory connections between the orbitofrontal cortex (OFC) and thalamus, decreasing abnormal reverberant activity within the OFC-caudate-pallidal-thalamic circuit. In this study, we investigated these connections using diffusion magnetic resonance imaging (dMRI) on human connectome datasets of twenty-nine healthy young-adult volunteers with two-tensor unscented Kalman filter based tractography. We studied the morphology of the lateral and medial orbitofrontothalamic connections and estimated their topographic variability within the VC/VS region. Our results showed that the morphology of the individual orbitofrontothalamic fibers of passage in the VC/VS region is complex and inter-individual variability in their topography is high. We applied this method to an example OCD patient case who underwent DBS surgery, formulating an initial proof of concept for a tractography-guided patient-specific approach in DBS for medically intractable OCD. This may improve on current surgical practice, which involves implanting all patients at identical stereotactic coordinates within the VC/VS region

Incorporating and Compensating Cerebrospinal Fluid in Surface-Based Forward Models of Magneto- and Electroencephalography

MEG/EEG source imaging is usually done using a three-shell (3-S) or a simpler head model. Such models omit cerebrospinal fluid (CSF) that strongly affects the volume currents. We present a four-compartment (4-C) boundary-element (BEM) model that incorporates the CSF and is computationally efficient and straightforward to build using freely available software. We propose a way for compensating the omission of CSF by decreasing the skull conductivity of the 3-S model, and study the robustness of the 4-C and 3-S models to errors in skull conductivity. We generated dense boundary meshes using MRI datasets and automated SimNIBS pipeline. Then, we built a dense 4-C reference model using Galerkin BEM, and 4-C and 3-S test models using coarser meshes and both Galerkin and collocation BEMs. We compared field topographies of cortical sources, applying various skull conductivities and fitting conductivities that minimized the relative error in 4-C and 3-S models. When the CSF was left out from the EEG model, our compensated, unbiased approach improved the accuracy of the 3-S model considerably compared to the conventional approach, where CSF is neglected without any compensation (mean relative error 40%). The error due to the omission of CSF was of the same order in MEG and compensated EEG. EEG has, however, large overall error due to uncertain skull conductivity. Our results show that a realistic 4-C MEG/EEG model can be implemented using standard tools and basic BEM, without excessive workload or computational burden. If the CSF is omitted, compensated skull conductivity should be used in EEG

Expectation Propagation for Neural Networks with Sparsity-Promoting Priors

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