Functional MRI methods and findings in schizophrenia

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Functional MRI imaging methods have allowed researchers to combine neurocognitive testing with structural neuroanatomical measures, take into consideration both cognitive and affective paradigms, and subsequently create computer-aided diagnosis techniques and algorithms. [1] [2] Functional MRI has several benefits, such as its non-invasive quality, relatively high spatial resolution, and decent temporal resolution. One particular method used in recent research is resting-state functional magnetic resonance imaging, rs-fMRI. fMRI imaging has been applied to numerous behavioral studies for schizophrenia, the findings of which have hinted toward potential brain regions that govern key characteristics in cognition and affect.

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In a 'reformulation' of the binary-risk vulnerability model, researchers have suggested a multiple-hit hypothesis which utilizes several risk factors — some bestowing a greater probability than others — to identify at-risk individuals, often genetically predisposed for schizophrenia. [3] The process of defining clinical criteria of schizophrenia for early diagnosis has posed a great challenge for scientists. [4]

Methodology

A rapid increase of studies in schizophrenia have covered topics such as abnormal activity in "motor tasks, working memory attention, word fluency, emotion processing, and decision making." [5] In contrast to the abundance of research centered on positive symptoms of the disorder, fMRI research for schizophrenia primarily analyzes the 'failures' of the neural system and consequent behavioral deficits. [5] To confirm that a task activates identical regions in schizophrenia patients vs controls, the given task typically begins easy so that both patients and healthy comparison subjects perform close to 100% accuracy; the task is then increased in difficulty to distinguish activation between two groups with varying abilities of individuals. [5]

The 'basic symptoms' approach

The 'basic symptoms' approach for schizophrenia, which emerged from "retrospective descriptions of the prodromal phase," represents a framework for a large portion of fMRI research, which evaluates changes in cognition and sensory perception that may affect higher-level information processes. [6] [7] Some researchers oppose the tendency of researchers to attribute schizophrenia to higher-order processes like working memory, attention, and executive processing, instead choosing to inspect impairments in basic sensory and perceptual functions. [7] Deficits in basic sensory functions influence higher-order processes such as auditory emotion recognition, perceptual closure, object recognition, etc. [7] In the visual system, for example, rudimentary deficits in the function of the magnocellular system results in impairments in higher-order processes like perceptual closure, object recognition, and reading. [7] On the other hand, fMRI data has also suggested the opposite. In one study, researchers found significantly differing activity between healthy and schizophrenic patients in the left dorsal parietal cortex and left ventrolateral prefrontal cortex; as these regions are essential components of a frontal-parietal executive system, hypo-activity in these regions for schizophrenia patients during working memory tasks were theorized to be associated with deficits in executive functioning. [8]

Resting-state fMRI

The 'disconnectivity hypothesis' is a key theory, describing the failure of mechanisms underlying schizophrenia, specifically the failure to integrate information properly. [9] Functional connectivity, which fMRI evaluates, is defined as the coordination of activity between brain regions. It is measured as "temporal correlations of low frequency oscillations in the BOLD signal between anatomically distinct brain areas," and can reveal resting state networks. [10] The cause for the correlations in fMRI measurements is theorized to be due to "correlated firing rates of interconnected neurons." [11] Resting-state functional magnetic resonance imaging (rs-fMRI) has become a powerful tool to examine the functional connectivity of networks throughout the brain, such as the default mode network (DMN). [12]

Abnormal brain connectivity has long been theorized as a fundamental cause of psychosis in schizophrenia. [13] rs-fMRI can help evaluate regional interactions at rest, and whether there are altered, reduced, or hyperactive connections in psychiatric disorders, like schizophrenia. During resting-state fMRI experiments, participants are instructed to relax, stay awake, but not think of anything. It is important to note that resting state networks can change between eyes open and eyes closed conditions. [14] Researchers then measure spontaneous brain activation. [10] There are several advantages to studying the resting-state of brain networks — the primary reason being that spontaneous neural activity accounts for a majority of the brain's activity in contrast to task-based neural activity. [14] Additionally, rs-fMRI eliminates confounding effects such as differing performances between healthy subjects and patients in tasks; rs-fMRI also requires less movement than task-based fMRI studies. [14] Seed-based analysis/ROI approaches to analyzing functional connectivity are common in rs-fMRI for schizophrenia. A seed (region of interest) is first selected, and BOLD time series are then extracted from the seed and all other voxels. After preprocessing, the temporal correlation between the seed and other brain voxels is determined and a functional connectivity map is produced by the software. [10] Seed-based comparisons in rs-fMRI have revealed functional disconnectivity in schizophrenia patients in numerous studies, using different ROIs for their seeds — in general, schizophrenia patients show reduced connectivity. [10] This information is compatible with experiment findings suggesting reduced activation in the amygdala in schizophrenia patients during sadness mood induction, for example. [15]

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<span class="mw-page-title-main">Functional magnetic resonance imaging</span> MRI procedure that measures brain activity by detecting associated changes in blood flow

Functional magnetic resonance imaging or functional MRI (fMRI) measures brain activity by detecting changes associated with blood flow. This technique relies on the fact that cerebral blood flow and neuronal activation are coupled. When an area of the brain is in use, blood flow to that region also increases.

<span class="mw-page-title-main">Functional neuroimaging</span>

Functional neuroimaging is the use of neuroimaging technology to measure an aspect of brain function, often with a view to understanding the relationship between activity in certain brain areas and specific mental functions. It is primarily used as a research tool in cognitive neuroscience, cognitive psychology, neuropsychology, and social neuroscience.

Functional integration is the study of how brain regions work together to process information and effect responses. Though functional integration frequently relies on anatomic knowledge of the connections between brain areas, the emphasis is on how large clusters of neurons – numbering in the thousands or millions – fire together under various stimuli. The large datasets required for such a whole-scale picture of brain function have motivated the development of several novel and general methods for the statistical analysis of interdependence, such as dynamic causal modelling and statistical linear parametric mapping. These datasets are typically gathered in human subjects by non-invasive methods such as EEG/MEG, fMRI, or PET. The results can be of clinical value by helping to identify the regions responsible for psychiatric disorders, as well as to assess how different activities or lifestyles affect the functioning of the brain.

<span class="mw-page-title-main">Neuroimaging</span> Set of techniques to measure and visualize aspects of the nervous system

Neuroimaging is the use of quantitative (computational) techniques to study the structure and function of the central nervous system, developed as an objective way of scientifically studying the healthy human brain in a non-invasive manner. Increasingly it is also being used for quantitative research studies of brain disease and psychiatric illness. Neuroimaging is highly multidisciplinary involving neuroscience, computer science, psychology and statistics, and is not a medical specialty. Neuroimaging is sometimes confused with neuroradiology.

<span class="mw-page-title-main">Posterior cingulate cortex</span> Caudal part of the cingulate cortex of the brain

The posterior cingulate cortex (PCC) is the caudal part of the cingulate cortex, located posterior to the anterior cingulate cortex. This is the upper part of the "limbic lobe". The cingulate cortex is made up of an area around the midline of the brain. Surrounding areas include the retrosplenial cortex and the precuneus.

Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The nervous system is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected and communicate. Because these structures are extremely complex, methods within this field use a high-throughput application of functional and structural neural imaging, most commonly magnetic resonance imaging (MRI), electron microscopy, and histological techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected spanning the nervous system including the various areas of cortex, cerebellum, the retina, the peripheral nervous system and neuromuscular junctions.

<span class="mw-page-title-main">Default mode network</span> Large-scale brain network active when not focusing on an external task

In neuroscience, the default mode network (DMN), also known as the default network, default state network, or anatomically the medial frontoparietal network (M-FPN), is a large-scale brain network primarily composed of the dorsal medial prefrontal cortex, posterior cingulate cortex, precuneus and angular gyrus. It is best known for being active when a person is not focused on the outside world and the brain is at wakeful rest, such as during daydreaming and mind-wandering. It can also be active during detailed thoughts related to external task performance. Other times that the DMN is active include when the individual is thinking about others, thinking about themselves, remembering the past, and planning for the future.

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<span class="mw-page-title-main">Resting state fMRI</span> Type of functional magnetic resonance imaging

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Dynamic functional connectivity (DFC) refers to the observed phenomenon that functional connectivity changes over a short time. Dynamic functional connectivity is a recent expansion on traditional functional connectivity analysis which typically assumes that functional networks are static in time. DFC is related to a variety of different neurological disorders, and has been suggested to be a more accurate representation of functional brain networks. The primary tool for analyzing DFC is fMRI, but DFC has also been observed with several other mediums. DFC is a recent development within the field of functional neuroimaging whose discovery was motivated by the observation of temporal variability in the rising field of steady state connectivity research.

Bruce Rosen is an American physicist and radiologist and a leading expert in the area of functional neuroimaging. His research for the past 30 years has focused on the development and application of physiological and functional nuclear magnetic resonance techniques, as well as new approaches to combine functional magnetic resonance imaging (fMRI) data with information from other modalities such as positron emission tomography (PET), magnetoencephalography (MEG) and noninvasive optical imaging. The techniques his group has developed to measure physiological and metabolic changes associated with brain activation and cerebrovascular insult are used by research centers and hospitals throughout the world.

<span class="mw-page-title-main">MRI pulse sequence</span>

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<span class="mw-page-title-main">Vince Calhoun</span> American engineer and neuroscientist (Born 1967)

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References

  1. ↑ Morgan, Kevin D.; Dazzan, Paola; Morgan, Craig; Lappin, Julia; Hutchinson, Gerard; Suckling, John; Fearon, Paul; Jones, Peter B.; Leff, Julian; Murray, Robin M.; David, Anthony S. (August 2010). "Insight, grey matter and cognitive function in first-onset psychosis". The British Journal of Psychiatry. 197 (2): 141–148. doi: 10.1192/bjp.bp.109.070888 . ISSN   0007-1250. PMID   20679268. S2CID   17223664.
  2. ↑ Algumaei, Ali H.; Algunaid, Rami F.; Rushdi, Muhammad A.; Yassine, Inas A. (2022-05-24). "Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data". PLOS ONE. 17 (5): e0265300. Bibcode:2022PLoSO..1765300A. doi: 10.1371/journal.pone.0265300 . ISSN   1932-6203. PMC   9129055 . PMID   35609033.
  3. ↑ Davis, Justin; Eyre, Harris; Jacka, Felice N; Dodd, Seetal; Dean, Olivia; McEwen, Sarah; Debnath, Monojit; McGrath, John; Maes, Michael; Amminger, Paul; McGorry, Patrick D; Pantelis, Christos; Berk, Michael (2016-06-01). "A review of vulnerability and risks for schizophrenia: Beyond the two hit hypothesis". Neuroscience & Biobehavioral Reviews. 65: 185–194. doi:10.1016/j.neubiorev.2016.03.017. ISSN   0149-7634. PMC   4876729 . PMID   27073049.
  4. ↑ Jablensky, Assen (2010-09-30). "The diagnostic concept of schizophrenia: its history, evolution, and future prospects". Dialogues in Clinical Neuroscience. 12 (3): 271–287. doi:10.31887/DCNS.2010.12.3/ajablensky. PMC   3181977 . PMID   20954425.
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  6. ↑ Schultze-Lutter, Frauke; Theodoridou, Anastasia (February 2017). "The concept of basic symptoms: its scientific and clinical relevance". World Psychiatry. 16 (1): 104–105. doi:10.1002/wps.20404. PMC   5269478 . PMID   28127912.
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  8. ↑ Rudert, Thomas; Lohmann, Gabriele (December 2008). "Conjunction analysis and propositional logic in fMRI data analysis using Bayesian statistics". Journal of Magnetic Resonance Imaging. 28 (6): 1533–1539. doi:10.1002/jmri.21518. PMID   19025961. S2CID   206097466.
  9. ↑ Gomez-Pilar, Javier (30 June 2020). Characterization of neural activity using complex network theory : an application to the study of schizophrenia. Springer. ISBN   978-3-030-49900-6. OCLC   1176541540.
  10. 1 2 3 4 Li, Peng; Fan, Teng-Teng; Zhao, Rong-Jiang; Han, Ying; Shi, Le; Sun, Hong-Qiang; Chen, Si-Jing; Shi, Jie; Lin, Xiao; Lu, Lin (2017-07-14). "Altered Brain Network Connectivity as a Potential Endophenotype of Schizophrenia". Scientific Reports. 7 (1): 5483. Bibcode:2017NatSR...7.5483L. doi:10.1038/s41598-017-05774-3. ISSN   2045-2322. PMC   5511161 . PMID   28710394.
  11. ↑ Wu, Lei; Eichele, Tom; Calhoun, Vince D. (October 2010). "Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: A concurrent EEG-fMRI study". NeuroImage. 52 (4): 1252–1260. doi:10.1016/j.neuroimage.2010.05.053. ISSN   1053-8119. PMC   3059127 . PMID   20510374.
  12. ↑ Schneider, F.; Weiss, U.; Kessler, C.; Salloum, J.B.; Posse, S.; Grodd, W.; Müller-Gärtner, H.W. (November 1998). "Differential amygdala activation in schizophrenia during sadness". Schizophrenia Research. 34 (3): 133–142. doi:10.1016/s0920-9964(98)00085-1. ISSN   0920-9964. PMID   9850979. S2CID   13213002.
  13. ↑ Karbasforoushan, H.; Woodward, N.D. (2012-11-01). "Resting-State Networks in Schizophrenia". Current Topics in Medicinal Chemistry. 12 (21): 2404–2414. doi:10.2174/156802612805289863. PMID   23279179.
  14. 1 2 3 Yu, Qingbao; A. Allen, Elena; Sui, Jing; R. Arbabshirani, Mohammad; Pearlson, Godfrey; D. Calhoun, Vince (2012-11-01). "Brain Connectivity Networks in Schizophrenia Underlying Resting State Functional Magnetic Resonance Imaging". Current Topics in Medicinal Chemistry. 12 (21): 2415–2425. doi:10.2174/156802612805289890. PMC   4429862 . PMID   23279180.
  15. ↑ Barch, Deanna M.; Csernansky, John G. (2007-07-01). "Abnormal Parietal Cortex Activation During Working Memory in Schizophrenia: Verbal Phonological Coding Disturbances Versus Domain-General Executive Dysfunction". American Journal of Psychiatry. 164 (7): 1090–1098. doi:10.1176/ajp.2007.164.7.1090. ISSN   0002-953X. PMID   17606661.
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