Over the last decades, resting state functionalmagneticresonanceimaging (rs-fMRI) has been increasingly applied to identify functional biomarkers for mental disorders and brain pathology. During rs-fMRI, subjects are typically instructed to rest inside the scanner for 5 to 10 min and to think of nothing in particular. Despite promising new in- sights yielded by this method, experimental control over the subject's actual behavior during the resting state is low. For instance, it is unclear how well subjects manage to keep their eyes open and, if a ﬁxation cross is used, how stable their gaze remains over time. It has been shown that the blood oxygen level dependent (BOLD) signal in occipital regions dif- fers systematically between an eyes-closed and ﬁxation rs-fMRI condi- tion ( Bianciardi et al., 2009; Marx et al., 2004 ). Furthermore, a recent study about rs-fMRI connectivity patterns by Tagliazucchi and Laufs (2014) indicated that a considerable portion of (healthy) subjects falls asleep during typical resting state experiments, involving changes in cortical and subcortical connectivity ( Sämann et al., 2010; Spoormaker et al., 2010 ). Such vigilance-dependent changes may become particu- larly problematic when comparing rs-fMRI ﬁndings between healthy subjects and (psychiatric) patients with hypo- or hyper-arousal symptomatology.
The human brain is a complex system comprising billions of heavily interconnected neurons. Despite this vast number, the brain’s anatomy as well as the emerging dynamics is highly structured. In fact it is widely assumed that the spatially and temporally coordinated activity of large coherent neuronal populations give rise to higher order phenomena such as cognition and behavior (Engel et al. 2001). This coordinated and cumulative neuronal activity becomes manifest in large scale oscillations and fluctuations, which can be recorded noninvasively in humans with methods such as electroencephalography (EEG) and functionalmagneticresonanceimaging (fMRI).
Independent component analysis is a method for blind signal separation formed on the basis of assumed statistical independence of the source signals. The problem of blind source sep- aration or blind signal separation (BSS) appears in many contexts. Blind source separation is a class of explorative tools originally developed for the analysis of images and sound. BSS has received wide attention in various fields such as speech enhancement, geophysical data processing, data mining, wireless communications, image processing, and biomedical signal analysis and processing (EEG, MEG, fMRI). The method is called ’blind’ because it aims to recover source signals from mixtures with unknown coefficients. The most simple situation occurs for two speakers speaking simultaneously. Imagine that the mixture of their voices reaches two microphones, and one wants to separate both sources such that each detector registers only one voice. The problem is called the cocktail party problem which can also be extended to N people standing around and chatting with each other. This mixture of signals is recorded by N microphones. Again, the aim is to extract the voices of the speaker (the sources) from the mixture of speech signals without knowing the sources and the mixture process assuming that the voices are independent of each other. In this project the problem of BSS is applied to the field of functionalmagneticresonanceimaging (fMRI), especially to fMRI time series, For the fMRI time series it is assumed that the measured signal of neuronal activity are mixed linearly with multiple other signals like noise or movement artifacts, contributing to the measurement. The aim of blind signal sep- aration in fMRI is to detect the intrinsic signals, i.e. the neuronal activity, from the mixed signals measured during the fMRI study. ICA is a statistical approach of transforming multidimensional data into components that are as independent of each other as possible.
During the past decade, advances in functionalmagneticresonanceimaging (fMRI) driven neuroscience have led to the notion that the human brain is a network of functionally interconnected regions that share information continuously. A methodological development with major contribution to this understanding is resting-state fMRI (rs-fMRI), with which functional connectivity between brain regions can be inferred by temporal co-variation of spontaneous fluctuations of the fMRI blood oxygen level dependent (BOLD) signal during rest 1,2,3,4 . Resting- state functional connectivity (rs-FC), in turn, is hypothesized to reflect the brain´s intrinsic functional architecture, thereby providing the functional fundament for task-related brain processes, which can then be associated with behavior, and, for example, personality 5,6,7,8,9 . A variety of techniques exists to characterize the functional connectivity, including (among others) well-established approaches focusing on connections of specific brain regions (seed-based analyses), or relatively newer methods that examine and characterize the overall structure of brain networks with graph analysis routines 1,2,3,4 . Although these methods have revealed a wealth of new insights into the connectivity of the brain in both health and disease 10,11,12,13 , a thorough understanding of the brain´s connectome that can account for its massive number of interacting components and associations to individual behavior still remains an ongoing endeavor.
Within the brain mapping community, the problem of validity and repeatability of functional neuroimaging results has recently become a major issue. In 2016, the Committee on Best Practice in Data Analysis and Sharing from the Organization for Human Brain Mapping (OHBM) created recommendations for replicable research in neuroimaging, focused on magneticresonanceimaging and functionalmagneticresonanceimaging (fMRI). Here, “replication” is defined as “Independent researchers use independent data and … methods to arrive at the same original conclusion.” “Repeatability” is defined as repeated investigations performed “with the same method on identical test/measurement items in the same test or measuring facility by the same operator using the same equipment within short intervals of time” (ISO 3534-2:2006 3.3.5). An intermediate position between replication and repeatability is defined for “reproducibility”: repeated investigations performed “with the same method on identical test/measurement items in different test or measurement facilities with different operators using different equipment” (ISO 3534-2:2006 3.3.10). Further definitions vary depending on the focus, be it the “measurement stability,” the “analytical stability,” or the “generaliz- ability” over subjects, labs, methods, or populations.
Results and conclusion: Clear responses were found in the somatosensory cortex contra lateral within the post central gyrus during stimulation of the left hand. Considering the other three extremities no signi ﬁcant responses were found. Nevertheless, we conclude that a functionalMagneticResonanceImaging under anaesthesia is possible for patients with severe chronic disorders of consciousness and brain areas responding to stimuli can be detected.
Keywords: age-related hearing loss, functional neuroimaging, audiovisual speech, listening effort
Age-related hearing loss affects a large proportion of the elderly but often stays untreated for several years (1). The loss of hearing abilities at high frequencies leads to difficulties in understanding speech and increased listening effort, especially in multispeaker situations. The effects of reduced sensory input on the brain are well investigated in the congenitally deaf and those with profound hearing loss and demonstrate that the loss of auditory input induces changes in neural processing in and beyond the auditory cortex (2). A well-investigated phenomenon is crossmodal plasticity, evident as increased responses in auditory cortex to visual stimuli. Recently, evidence for crossmodal plasticity has also been found in mild to moderate age-related hearing loss (3, 4). For example, in a functionalmagneticresonanceimaging (fMRI) study in elderly subjects with varying degree of high frequency hearing loss, increased functional connectivity between auditory cortex and motion -related brain regions was found when subjects had to categorize frequency-modulated tones presented alone or in the context of non-matching versus matching visual motion (3). Together with a prior behavioural finding that hearing impaired listeners are more distracted by incongruent v isual input (5) the results
Although human beings encounter a plethora of everyday events, subjective experiences, and affective states, not all of these are transformed into permanent memories. Throughout the last two decades, cognitive neuroscientists have employed various methods, ranging from animal behaviorism to functionalimaging studies, to disentangle how we form and retrieve permanent memories, which essentially make up the core of our personal history and individual identity. In order to compare events that will later be remembered to those that will be forgotten, a widely used experimental design was developed in the context of event-related functionalmagneticresonanceimaging (fMRI). It employs an encoding phase, during which stimuli such as words or pictures are presented, and a recognition phase, or subsequent memory test, during which participants have to indicate which stimuli have already been presented in the encoding phase and which ones are new. Typically, a comparison and respective blood oxygenation level dependent (BOLD) activation contrast is based on later performance levels (remembered vs. forgotten;
Anticipation of upcoming events is an important skill allowing individuals to develop adaptive responses for their homeostasis and survival in a changing environment. In several psychiatric diseases this adaptive mechanism is impaired leading to well-known symptoms, like excessive worry and pessimistic views on future. Although this is an established model for clinicians dealing with such patients, the neural correlates of this mechanism have only been discovered recently, using brain imaging methods. In this dissertation three functionalmagneticresonanceimaging studies, using different paradigms that are known to elicit certain emotions or
two streams: the dorsal (occipito-parietal) stream for object recognition and the ventral (occipito-temporal) stream for motion and spatial information processing (Barton, 2011). Later functionalmagneticresonanceimaging (Altman, Bülthoff, & Kourtzi, 2003; Dumoulin & Hess, 2006; Kourtzi & Huberle, 2005; Kourtzi, Tolias, Altmann, Augath, & Logothetis, 2003; Malach et al., 1995) and electroencephalographic studies (Herrmann & Bosch, 2001; Machilsen, Novitskiy, Vancleef, & Wagemans, 2011; Volberg & Greenlee, 2014) found that both early and higher visual areas are associated with the process of Gestalt perception: it was demonstrated that early visual areas were correlated with processing of local information (e.g. orientation and contour of Gabor elements) while higher visual areas showed responses to perception of the global information (shape of the contour) of the stimuli. Besides parieto-occipital and occipito-temporal areas of the brain, the temporoparietal junction (TPJ) is thought to play a major role in global Gestalt perception (Huberle & Karnath, 2012). A bilateral representation of Gestalt perception in the TPJ has been proposed, but different clusters within the TPJ were found to be involved (Renning, Bilalić, Huberle, Karnath, & Himmelbach, 2013; Ritzinger, Huberle, & Karnath, 2012). In a recent study the role of the right anterior TPJ in processing novel global forms has been identified (Renning, Himmelbach, Huberle, & Karnath, 2015). In contrast, another study using perceptual alternations between the perception of local dot motion and global illusionary square motion found reduced beta-band power in the posterior parietal cortex during the perceptual grouping phase (Zaretskaya & Bartels, 2015). Hence, an exact localization of where ‘Gestalts’ are formed in the human brain remains unresolved.
In the focused application, impaired patients listen to an audio book to get an objective measurement for visual field defects and/or changes in retinal representations. The focus was to be able to use whole brain data, accordingly also audio and more complex data, to transfer visual ROIs. The importance for the application to visually impaired patients lies in the avoidance of fixation dependence methods i.e., retinotopic mapping via traveling wave [Engel et al., 1997] or pRF paradigm [Dumoulin and Wandell, 2008]. An alternative could be in using naturalistic/complex stimuli [Dubois and Adolphs, 2016]. Using more “interesting”stimuli additionally make it easier for the patients to maintain attention to the stimulus. Anatomical alignment would only transform the functional data on the basis of structural information. Changes in the functional brain activity, i.e., visual impairments, are not covered by this method of transformation. The transformation matrix was derived from the activation patterns (time series) of the ROIs’ voxel that should be transferred. To sum every preference of a preferred stimulus, a movie would be the best for the purpose of generating visual activation with an fixation-independent stimulus. Therefore, data from the presentation of a pure audio-movie and an audio-visual movie has been chosen [Hanke et al., 2014, 2016] as well. Therefore, I undertook the following steps:
maintained during the entire MRI sessions via a respiratory mask connected to an OxyMount hypoxia machine located outside of the scanner (Oxy Mount, Mountain Air 6001 /XA; OxyTherm GmbH, Coburg, Germany). The participants also wore this mask during the baseline condition without being connected to the hypoxia machine, to ensure identical conditions. SaO2 and heart rate were measured continuously using a finger-mounted pulse oximeter clip on the left index finger (9550 Onyx II; Nonin Medical, Plymouth, USA). MR imaging was performed on a 3 Tesla standard clinical MR scanner (Signa HDx, GE Healthcare, Milwaukee, USA) with 8 receiving channels. Structural imaging was performed using a T 1 -weighted fast spoiled gradient-echo
Abstract: Functional MRI is valuable in presurgical planning due to its non-invasive nature, repeatabil- ity, and broad availability. Using ultra-high field MRI increases the specificity and sensitivity, increas- ing the localization reliability and reducing scan time. Ideally, fMRI analysis for this application should identify unreliable runs and work even if the patient deviates from the prescribed task timing or if there are changes to the hemodynamic response due to pathology. In this study, a model-free analysis method—UNBIASED—based on the consistency of fMRI responses over runs was applied, to ultra-high field fMRI localizations of the hand area. Ten patients with brain tumors and epilepsy underwent 7 Tesla fMRI with multiple runs of a hand motor task in a block design. FMRI data were analyzed with the proposed approach (UNBIASED) and the conventional General Linear Model (GLM) approach. UNBIASED correctly identified and excluded fMRI runs that contained little or no activation. Generally, less motion artifact contamination was present in UNBIASED than in GLM results. Some cortical regions were identified as activated in UNBIASED but not GLM results. These were confirmed to show reproducible delayed or transient activation, which was time-locked to the task. UNBIASED is a robust approach to generating activation maps without the need for assumptions about response timing or shape. In presurgical planning, UNBIASED can complement model-based methods to aid surgeons in making prudent choices about optimal surgical access and resection mar- gins for each patient, even if the hemodynamic response is modified by pathology. Hum Brain Mapp 38:3163–3174, 2017. V C 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
The left STG plays a major role in speech perception and production [Price, 2012] and serves as an interface between multimodal sensory and motor regions [Hickok and Poeppel, 2007]. Furthermore, it was shown that the left STG is concerned with the integration of auditory and visual information [Van Atteveldt et al., 2004], which is an essential part of successful reading acquisition. Both dys- lexic children and dyslexic adults were found to exhibit left STG dysfunctions during letter-speech sound integra- tion [Blau et al., 2009, 2010]. Recently, combining multi- voxel pattern analysis and functional and structural connectivity analysis, Boets et al.  reported that the problem of dyslexic readers does not concern the integrity of stored phonetic representations in the left STG per se, but the access to these representations via left IFG regions. As mentioned earlier, Paulesu et al.  reported higher STG activation in readers of a shallow orthography
The ability to infer another person’s emotions from his or her intention is closely developed around the ability to understand false beliefs, the key Theory of Mind (ToM) ability. False belief understanding is acquired at the age of 4- to 5-years and is supposed to be an indicator of representational understanding (for reviews on ToM in developmental psychology see Astington, 1993; Perner, 1991b; Sodian & Thoermer, 2006; Wellman, 1990; Wellman, Cross, & Watson, 2001). By the age of 2½ to 4 years, children begin to attribute emotions based on processing simple intention- outcome-relations. Inferring emotions from simple intention-outcome-relations is assumed to not require representational operations (Astington, 1999a; Astington, 2001b; Baird & Astington, 2005; Perner, 1991a). From the age of six to seven years, children begin to properly integrate others’ immoral intentions into intention- outcome relations. Developmental evidence indicates that the ability to infer emotions from other’s immoral intentions is based on the development of representational understanding (Baird & Astington, 2004; Sokol, 2004; Sokol & Chandler, 2004; Sokol, Chandler, & Jones, 2004). This thesis is the first that investigates the neural correlates associated with inferring emotions based on mental states such as intentions. By identifying the brain regions associated with intention- based emotion attribution, functional neuroimaging can help clarify whether intention-based emotion attribution is associated with common or distinct neural networks relative to false belief understanding.
With an increasing number of ultra-high field MR systems worldwide possibilities of higher spatial and temporal resolution in combination with increased sensitivity and specificity are expected to advance detailed imaging of distinct cortical brain areas and subcortical structures. One target region of particular importance to applications in psychiatry and psychology is the amygdala. However, ultra-high field magneticresonanceimaging of these ventral brain regions is a challenging endeavor that requires particular methodological considerations. Ventral brain areas are particularly prone to signal losses arising from strong magnetic field inhomogeneities along susceptibility borders. In addition, physiological artifacts from respiration and cardiac action cause considerable fluctuations in the MR signal. Here we show that, despite these challenges, fMRI data from the amygdala may be obtained with high temporal and spatial resolution combined with increased signal-to-noise ratio. Maps of neural activation during a facial emotion discrimination paradigm at 7T are presented and clearly show the gain in percental signal change compared to 3T results, demonstrating the potential benefits of ultra-high field functional MR imaging also in ventral brain areas. 1
Chapter 3. FunctionalMagneticResonanceImaging (fMRI)
so-called fat suppression techniques (cf. section 8.2).
3.2.5. N/2 Ghost
A specific EPI artefact is the so called N/2 ghost caused by a misregistration between odd and even lines acquired. The N/2 ghost is a rather faint duplicate of the imaged object shifted by half the field-of-view. The intensity of the N/2 ghost is usually modulated by a sinosoidal signal profile in readout direction which reflects the phase difference corresponding to the shift between the k-space lines. Various methods have been proposed to correct for these ghosts, some of which do not even require a separate calibration scan. The most prevalent calibration method relies on the acquisition of two non-phase-encoded projections using a very short echo train immediately following excitation (and slice rewinding). Assuming, without loss of generality, the first readout is positive, the second, negative readout is usually followed by an additional third positive readout resulting in three projections with extrememly short echo time and thus very little phase evolution (almost no signal drop-outs, etc.). The first and the third projection, i.e. the Fourier transformed odd numbered signals, are averaged and the complex phase is compared to the complex phase of the second projection.The systematic phase difference can then be corrected for in the following imaging scans. Unless stated otherwise, for all results shown in this work a linear approximation of the phase (constant offset and slope) is used for phase correction. This leads to reasonable N/2 ghost reduction, at least at moderate field strengths of 3 Tesla. Alternative methods include non-linear phase correction and a pixel-by-pixel phase correction which can be computed from standard phase correction scans as well. A detailed discussion of these fundametal phase correction methods can be found in Ref. .
Chapter 3. FunctionalMagneticResonanceImaging (fMRI)
so-called fat suppression techniques (cf. section 8.2).
3.2.5. N /2 Ghost
A specific EPI artefact is the so called N /2 ghost caused by a misregistration between odd and even lines acquired. The N /2 ghost is a rather faint duplicate of the imaged object shifted by half the field-of-view. The intensity of the N /2 ghost is usually modulated by a sinosoidal signal profile in readout direction which reflects the phase difference corresponding to the shift between the k-space lines. Various methods have been proposed to correct for these ghosts, some of which do not even require a separate calibration scan. The most prevalent calibration method relies on the acquisition of two non-phase-encoded projections using a very short echo train immediately following excitation (and slice rewinding). Assuming, without loss of generality, the first readout is positive, the second, negative readout is usually followed by an additional third positive readout resulting in three projections with extrememly short echo time and thus very little phase evolution (almost no signal drop-outs, etc.). The first and the third projection, i.e. the Fourier transformed odd numbered signals, are averaged and the complex phase is compared to the complex phase of the second projection.The systematic phase difference can then be corrected for in the following imaging scans. Unless stated otherwise, for all results shown in this work a linear approximation of the phase (constant offset and slope) is used for phase correction. This leads to reasonable N/2 ghost reduction, at least at moderate field strengths of 3 Tesla. Alternative methods include non-linear phase correction and a pixel-by-pixel phase correction which can be computed from standard phase correction scans as well. A detailed discussion of these fundametal phase correction methods can be found in Ref. .