In cases of temporal lobe epilepsy, seizures are often controlled by anterior temporal lobe resection. However, an assessment of the impact of surgery on language is required. Currently-used assessments are either non-specific within regions or use functional magnetic resonance imaging (fMRI), which can suffer signal distortion in the temporal lobes due to the presence of airways. Magnetoencephalography (MEG) shows a complimentary sensitivity, but has not been used for laterality assessment. We present a method that combines fMRI with MEG for optimized sensitivity. MEG activation maps were generated using a beamformer, showing activity in the anterior temporal lobes and lateral occipital cortex. fMRI showed activation in medial temporal lobe regions, the frontal poles and the hippocampus, an area of clinical concern during surgical planning. The present study introduces a method for integrating MEG and fMRI activation to create high-resolution laterality maps in regions of concern for epilepsy.

The Blood Oxygen Level Dependent (BOLD) signal provides indirect estimates of neural activity. The parameters of this BOLD signal can give information about the pathophysiological state of the brain. Most of the models for the BOLD signal are overparameterized which makes the unique identification of these parameters difficult. In this work, we use information from multiple neu- roimaging sources to get better estimates of these parameters instead of relying on the information from the BOLD signal only. The mulitmodal neuroimaging setup consisted of the information from Cerebral Blood Volume (CBV) ( VASO-Fluid-Attenuation-Inversion-Recovery (VASO-FLAIR)), and Cerebral Blood Flow (CBF) (from Arterial Spin Labelling (ASL)) in addition to the BOLD signal and the fusion of this information is achieved in a Particle Filter (PF) framework. The trace plots and the correlation coefficients of the parameter estimates from the PF reflect ill-posedness of the BOLD model. The means of the parameter estimates are much closer to the ground truth compared to the estimates obtained using only the BOLD information. These parameter estimates were also found to be more robust to noise and influence of the prior. / Master of Science

Functional neuroimaging techniques such as near-infrared spectroscopy (NIRS) have been studied in brain-computer interface (BCI) development. Previous research has suggested that the addition of a second brain-monitoring modality may improve the accuracy of a NIRS-BCI. The objective of this study was to determine whether the classification accuracies achievable by a multimodal BCI, which combines NIRS and transcranial Doppler ultrasonography (TCD) signals, can exceed those attainable using a unimodal NIRS-BCI or TCD-BCI. Nine able-bodied subjects participated in the study. Simultaneous measurements were made with NIRS and TCD instruments while participants were prompted to perform a verbal fluency task or to remain at rest, within the context of a block-stimulus paradigm. In five of nine (55.6%) participants, classification accuracies with the NIRS-TCD system were significantly higher (p<0.05) than with NIRS or TCD systems alone. Our results suggest that multimodal neuroimaging may be a promising approach towards improving the accuracy of future BCIs.

Functional neuroimaging techniques such as near-infrared spectroscopy (NIRS) have been studied in brain-computer interface (BCI) development. Previous research has suggested that the addition of a second brain-monitoring modality may improve the accuracy of a NIRS-BCI. The objective of this study was to determine whether the classification accuracies achievable by a multimodal BCI, which combines NIRS and transcranial Doppler ultrasonography (TCD) signals, can exceed those attainable using a unimodal NIRS-BCI or TCD-BCI. Nine able-bodied subjects participated in the study. Simultaneous measurements were made with NIRS and TCD instruments while participants were prompted to perform a verbal fluency task or to remain at rest, within the context of a block-stimulus paradigm. In five of nine (55.6%) participants, classification accuracies with the NIRS-TCD system were significantly higher (p<0.05) than with NIRS or TCD systems alone. Our results suggest that multimodal neuroimaging may be a promising approach towards improving the accuracy of future BCIs.

Doctoral thesis assessing the effects of one-week of escitalopram administration on healthy humans during sequence motor learning training. Published in 3 research articles.

Social anxiety disorder (SAD) is a common psychiatric disorder associated with considerable suffering. Cognitive behaviour therapy (CBT) has been shown to be effective but a significant proportion does not respond or relapses, stressing the need of augmenting treatment. Using neuroimaging could elucidate the psychological and neurobiological interaction and may help to improve current therapeutics. To address this issue, functional and structural magnetic resonance imaging (MRI) were repeatedly conducted on individuals with SAD randomised to receive CBT or an active control condition. MRI was performed pre-, and post-treatment, as well as at one-year follow-up. Matched healthy controls were also scanned to be able to evaluate disorder-specific neural responsivity and structural morphology. This thesis aimed at answering three major questions. I) Does the brain’s fear circuitry (e.g., the amygdala) change, with regard to neural response and structural morphology, immediately after CBT? II) Are the immediate changes in the brain still present at long-term follow-up? III) Can neural responsivity in the fear circuitry predict long-term treatment outcome at the level of the individual? Thus, different analytic methods were performed. Firstly, multimodal neuroimaging addressed questions on concomitant changes in neural response and grey matter volume. Secondly, two different experimental functional MRI tasks captured both neural response to emotional faces and self-referential criticism. Thirdly, support vector machine learning (SVM) was used to evaluate neural predictors at the level of the individual. Amygdala responsivity to self-referential criticism was found to be elevated in individuals with SAD, as compared to matched healthy controls, and the neural response was attenuated after effective CBT. In individuals with SAD, amygdala grey matter volume was positively correlated with symptoms of anticipatory speech anxiety, and CBT-induced symptom reduction was associated with decreased grey matter volume of the amygdala. Also, CBT-induced reduction of amygdala grey matter volume was evident both at short- and long-term follow-up. In contrast, the amygdala neural response was weakened immediately after treatment, but not at one-year follow-up. In extension to treatment effects on the brain, pre-treatment connectivity between the amygdala and the dorsal anterior cingulate cortex (dACC) was stronger in long-term CBT non-responders, as compared to long-term CBT responders. Importantly, by use of an SVM algorithm, pre-treatment neural response to self-referential criticism in the dACC accurately predicted (&gt;90%) the clinical response to CBT. In conclusion, modifying the amygdala is a likely mechanism of action in CBT, underlying the anxiolytic effects of this treatment, and the brain’s neural activity during self-referential criticism may be an accurate and clinically relevant predictor of the long-term response to CBT. Along these lines, neuroimaging is a vital tool in clinical psychiatry that could potentially improve clinical decision-making based on an individual’s neural characteristics. / Social ångest är en av de vanligaste psykiska sjukdomarna. Mer än en miljon svenskar bedöms lida av detta. Social ångest leder ofta till svåra konsekvenser för den som drabbas, men även ökade kostnader för samhället har noterats, t ex i form av ökad sjukfrånvaro. Även om många som drabbas inte söker hjälp så finns effektiva behandlingar för social ångest, både farmakologiska och psykologiska behandlingar rekommenderas av Socialstyrelsen. Kognitiv beteendeterapi (KBT) är en evidensbaserad och rekommenderad psykologisk behandling för social ångest. Trots att nuvarande interventioner är effektiva så är det fortfarande en andel individer som inte blir förbättrade. Det finns en stor andel studier som visar att individer med social ångest, i jämförelse med friska individer, karakteriseras av överdriven aktivitet i ett nätverk som har till uppgift att tolka och reagera på hotfull information. Denna aktivitet är lokaliserad i rädslonätverket där området amygdala spelar en central roll. Det finns ett behov att utveckla nuvarande behandlingar och denna avhandling syftar till att öka vår förståelse för en neurobiologisk verkningsmekanism bakom KBT för social ångest. I detta forskningsprojekt har magnetresonanstomografi (MRT) använts för att undersöka personer som lider av social ångest. Upprepade mätningar har genomförts, innan, efter, och vid uppföljning ett år efter ångestlindrande behandling. Utöver detta har individer som inte lider av social ångest undersökts för att förstå hur patienter skiljer sig från friska personer, men också för att undersöka om behandlingen normaliserar patientens hjärna. Under tiden som deltagarna undersöktes med MRT genomfördes två experiment för att ta reda på hur hjärnan reagerar på affektiv information. Deltagarna tittade på bilder med ansikten som uttrycker emotioner, t ex arga och rädda ansiktsuttryck, samt information som innehöll kritiska kommentarer riktade till personen själv eller någon annan, t ex ”ingen tycker om dig” eller ”hon är inkompetent”. Strukturella bilder på deltagarnas hjärnor har också samlats in vid varje mättillfälle. Utöver detta fick alla deltagare instruktioner om att de efter MRT skulle hålla en muntlig presentation inför en publik. Denna uppgift är oftast den värsta tänkbara för individer med social ångest, och syftet med uppgiften var att relatera hjärnans struktur och aktivitet till hur mycket ångest som individerna upplevde inför denna situation. I arbetet med denna avhandling har tre frågor ställts. a) Uppstår strukturella och funktionella förändringar i rädslonätverket direkt efter avslutad KBT (Studie I och II)? b) Är de tidiga förändringarna efter behandlingen även kvarstående ett år senare (Studie III)? c) Kan hjärnans reaktioner i rädslonätverket förutspå vilka individer som kommer att bli förbättrade av en ångestlindrande psykologisk behandling på lång sikt? Resultat från studierna i denna avhandling sammanfattas nedan: Reaktioner till självriktad kritik i amygdala är överdrivna hos individer med social ångest, i jämförelse med friska individer Reaktioner i amygdala minskar efter att individerna blivit behandlade med KBT och minskningarna korrelerar till minskade symptom av social ångest Den strukturella volymen av amygdala korrelerar positivt med hur mycket ångest individerna upplever inför en muntlig presentation, och minskningen av dessa symptom korrelerar även med hur mycket volymen av amygdala minskar efter KBT Minskningen av amygdalavolym och den samtidigt minskade reaktiviteten i amygdala till självriktad kritik är korrelerade. Medieringsanalyser antyder att det är den minskade volymen som driver förhållandet mellan minskad reaktivitet och minskad ångest inför att hålla en muntlig presentation Den strukturella minskningen av amygdala ses både direkt efter behandlingens avslut, men även vid uppföljning ett år senare. Hjärnans reaktivitet till självriktad kritik i amygdala minskar direkt efter behandling, men är inte kvarstående vid uppföljning ett år senare Kopplingen mellan hjärnans reaktivitet till självriktad kritik i amygdala och dorsala främre cingulum var starkare hos de som inte blev förbättrade (jämfört med de som blev bättre) av en ångestlindrande behandling på lång sikt Med hjälp av en stödvektormaskin (en. support vector machine learning) och ett mönster av hjärnaktivitet i dorsala främre cingulum innan behandling påbörjades, predicerades (med 92% träffsäkerhet) vilka individer som ett år senare var fortsatt förbättrade av en effektiv psykologisk behandling Utifrån dessa observationer är slutsatserna att strukturell och funktionell påverkan på amygdala är en möjlig neurobiologisk mekanism för minskad social ångest efter KBT, samt att reaktivitet i främre cingulum kan ge kliniskt relevant data om vem som kommer att bli förbättrad av en psykologisk behandling. Denna information kan potentiellt vara viktig i framtidens psykiatri för att utveckla existerande behandlingar, men även för att stödja klinikers beslutsfattande huruvida en viss individ bör erbjudas en specifik behandling eller ej. / <p>Illustration on the cover by Jan Lööf. Cover image printed with permission from Jan Lööf and Bonnier Carlsen Förlag. The cover was art directed by Staffan Lager.</p><p>The thesis is reprinted and the previous ISBN was 9789176856888.</p>

Functional brain connectivity is a marker of the brain state. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during EEG data analysis may leave part of the neural activity unrecognized. A proposed approach blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. The blind decomposition of EEG spectrogram by Parallel Factor Analysis has been shown to be a useful technique for uncovering patterns of neural activity where each pattern contains three signatures (spatial, temporal, and spectral). The decomposition takes into account the trilinear structure of EEG data, as compared to the standard approaches of electrode averaging, electrode subset selection or using standard frequency bands. The simultaneously acquired BOLD fMRI data were decomposed by Independent Component Analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and functional connectivity network states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of functional connectivity network states and the fluctuations of EEG spectral patterns. Three patterns related to the dynamics of functional connectivity network states were found. Previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. This work suggests that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.