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system of ordinary differential equations describing the causal relationship between them, although many parameters (and relationships) will be initially unknown. Using previous results on how neural activity is known to translate into fMRI or EEG signals, one can take the measured signal and determine the likelihood that model parameters have particular values. The elucidated model can then be used to predict relationships between the considered brain regions under different conditions. A key factor to consider during the design of neuroimaging experiments involving DCM is the relationship between the timing of tasks or stimuli presented to the subject and the ability of DCM to determine the underlying relationships between brain regions, which is partially determined by the temporal resolution of the imaging modality in use.
125:
experimental limitations. Some previous work has focused on attempting to use the high spatial resolution of fMRI to determine the (spatial) origin of EEG/MEG signals, so that in future work this spatial information could be extracted from a unimodal EEG/MEG signal. While some studies have seen success in correlating signal origins between modalities to within a few millimeters, the results have not been uniformly positive. Another current limitation is the actual experimental setup: taking measurements using both modalities at once yields inferior signals, but the alternative of measuring each modality separately is confounded by trial-to-trial variability.
315:. Human subjects were first split into a control and experimental group. The control group was presented with letters in a language they did not understand, and non-linguistic visual diagrams. The experimental group was tasked with two activities: the first activity was to remember a string of letters, and was intended activate all elements of the phonological loop. The second activity asked participants to assess whether given phrases rhymed, and was intended to only activate certain sub-systems involved in vocalization, but specifically not the phonological storage.
23:
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
203:. An important consideration with SPM, however, is that the large number of comparisons requires one to control the false positive rate with a more stringent significance threshold. This can be done either by modifying the initial statistical test to decrease the Ξ± value so as to make it harder for a particular voxel to exhibit a significant difference (e.g.,
294:
resulted in a model capable of explaining 53% of the change in verbal IQ as a function of grey matter density in the left motor cortex. The study also observed the previously reported phenomenon that a ranking of young subjects by IQ does not stay constant as the subjects age, which confounds any measurement of the efficacy of educational programs.
273:, though there was no learning-related change in any visual area. Combining fMRI with DCM on subjects performing a learning task allows one to delineate which brain systems are involved in various sorts of learning, whether implicit or explicit, and document for long these tasks lead to changes in resting-state brain activation.
343:
activation markers for specific psychiatric illnesses, and also aid in the development of therapeutics and animal models. While a true baseline of brain function in psychiatric patients is near-impossible to obtain, reference values can still be measured by comparing images gathered from patients before and after treatment.
310:
is a component of working memory that stores a small set of words that can be maintained indefinitely if not distracted. The concept was proposed by the psychologists Alan
Baddeley and Graham Hitch to explain how phrases or sentences can be internalized and used to direct action. By using statistical
109:
PET works by introducing a radiolabeled biologically active molecule. The choice of molecule dictates what is visualized: by using a radiolabeled analog of glucose, for example, one can obtain an image whose intensity distribution indicates metabolic activity. PET scanners offer sampling rates in the
57:
dimensions on the order of a few millimeters, but their relatively low sampling rate hinders the observation of rapid and transient interactions between distant regions of the brain. These temporal limitations are overcome by MEG, but at the cost of only detecting signals from much larger clusters of
318:
By comparing the first experimental task to the second, as well as to the control group, the study authors observed that the brain region most significantly activated by the task requiring phonological storage was the supramarginal gyrii. This result was backed up by previous literature observations
259:
Many previous fMRI studies have seen that spontaneous activation of functionally connected brain regions occurs during the resting state, even in the absence of any sort of stimulation or activity. Human subjects presented with a visual learning task exhibit changes in functional connectivity in the
141:
connectivity. Two brain regions are said to be functionally connected if there is a high correlation between the times that the two are firing, though this does not imply causality. Effective connectivity, on the other hand, is a description of the causal relationship between various brain regions.
22:
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 β numbering in the thousands or millions β fire
183:
Statistical parametric mapping (SPM) is a method for determining whether the activation of a particular brain region changes between experimental conditions, stimuli, or over time. The essential idea is simple, and consists of two major steps: first, one performs a univariate statistical test on
342:
Montague et al. note that the almost "unreasonable effectiveness of psychotropic medication" has somewhat stymied progress in this field, and advocate for a large-scale "computational phenotyping" of psychiatric patients. Neuroimaging studies of large numbers of these patients could yield brain
285:
and were fMRI scanned over the course of 3.5 years, and had their IQ predicted by the level of grey matter localization. This study was well-conducted, but studies of this sort frequently suffer from "double-dipping," where a single dataset is used both to identify the brain regions of interest
168:
Dynamic causal modeling (DCM) is a
Bayesian method for deducing the structure of a neural system based on the observed hemodynamic (fMRI) or electrophysiologic (EEG/MEG) signal. The first step is to make a prediction as to the relationships between the brain regions of interest, and formulate a
322:
Though this study was able to precisely localize a specific function anatomically and the methods of functional integration and imaging are of great value in determining the brain regions involved in certain information processing tasks, the low-level neural circuitry that gives rise to these
297:
These studies can be cross-validated by attempting to locate and assess patients with lesions or other damage in the identified brain region, and examining whether they exhibit functional deficits relative to the population. This methodology would be hindered by the lack of a "before" baseline
293:
The study authors avoided double-dipping by using a "leave-one-out" methodology, that involves building a predictive model for each of the n members of a sample based on data from the other n-1 members. This ensures that the model is independent of the subject whose IQ is being predicted, and
233:
Voxel-based morphometry (VBM) is a method that allows one to measure brain tissue composition differences between subjects. To do so, one must first register all images to a standard coordinate system, by mapping them to a reference image. This is done by use of an affine transformation that
124:
Multimodal imaging frequently consists of the coupling of an electrophysiologic measurement technique, such as EEG or MEG, with a hemodynamic one such as fMRI or PET. While the intention is to use the strengths and limitations of each to complement the other, current approaches suffer from
94:
Magnetoencephalography (MEG) is an imaging modality that uses very sensitive magnetometers to measure the magnetic fields resulting from ionic currents flowing through neurons in the brain. High-quality MEG machines allow for sub-millisecond sampling rates.
145:
While statistical assessment of the functional connectivity of multiple brain regions is non-trivial, determining the causality of which brain regions influence which to fire is much thornier, and requires solutions to ill-posed optimization problems.
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studies have even shown changes in functional connectivity during a single scan. By taking fMRI scans of subjects before and after the learning task, as well as on the following day, it was shown that the activity had caused a resting-state change in
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The ability to visualize whole-brain activity is frequently used in comparing brain function during various sorts of tasks or tests of skill, as well as in comparing brain structure and function between different groups of people.
207:), or by modifying the clustering analysis in the second step by only considering a brain region's activation to be significant if it contains a certain number of voxels that exhibit a statistical difference (see
39:. 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.
311:
parametric mapping to assess differences in cerebral blood flow between participants performing two different tasks, Paulescu et al. were able to identify the storage of the phonological loop as in the
1205:. Proceedings of the NATO Advanced Research Workshop on Psychiatric Neuroimaging, 29 September-1 October 2002, Chiavari, Italy --T.p. verso. Amsterdam; Washington, DC: IOS Press. pp. 55β59.
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have yielded some insight into the changes in effective connectivity caused by these diseases, a comprehensive understanding of the functional remodelling that occurs has not yet been achieved.
451:
359:
Luca, M.; Beckmann, CF; De
Stefano, N; Matthews, PM; Smith, SM (2006). "fMRI resting state networks define distinct modes of long-distance interactions in the human brain".
153:
Diagram showing the relationship between the experimental input function, u(t), neuronal activity x(t), and the observed hemodynamic or electrophysiologic response, y(t).
195:
There is great flexibility in the choice of statistical test (and thus the questions that an experiment can be designed to answer), and common choices include the
281:
Voxel-based morphometric measurements of grey matter localization in the brain can be used to predict components of IQ. A set of 35 teenagers were tested for
53:
A study's choice of imaging modality depends on the desired spatial and temporal resolution. fMRI and PET offer relatively high spatial resolution, with
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in a voxel can be determined by intensity. This allows one to compare the tissue composition of corresponding brain regions between different subjects.
72:
Functional magnetic resonance imaging (fMRI) is a form of MRI that is most frequently used to take advantage of a difference in magnetism between
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307:
67:
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Rosa, MJ; Daunizeau, J; Friston, KJ (2010). "EEG-fMRI integration: a critical review of biophysical modeling and data analysis approaches".
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minimizes the sum-of-squares intensity difference between the experimental image and the reference. Once this is done, the proportion of
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Buxton, RB; Wong, EC; Frank, LR (1998). "Dynamics of blood flow and oxygenation changes during brain activation: the balloon model".
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differences, and determines which brain regions exhibit different levels of activation under different experimental conditions.
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activity. Dynamic causal modeling revealed that the hippocampus also exhibited a new level of effective connectivity with the
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to assess blood flow to different parts of the brain. Typical sampling rates for fMRI images are in the tenths of seconds.
104:
36:
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Paulesu E, Frith CD, Frackowiak RS (March 1993). "The neural correlates of the verbal component of working memory".
407:"Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain"
218:
fMRI was used to detect whether PTSD affects grey:white matter ratio in women with
Borderline Personality Disorder.
1100:"Exploring the psychosis functional connectome: aberrant intrinsic networks in schizophrenia and bipolar disorder"
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to develop a predictive model, which leads to overtraining of the model and an absence of real predictive power.
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Friston, K.; Holmes, A. P.; Worsley, K. J.; Poline, J.-P.; Frith, C. D.; Frackowiak, R. S. J. (1995).
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between each experimental condition. Second, one analyzes the clustering of the voxels that show
948:"Early visual learning induces long-lasting connectivity changes during rest in the human brain"
522:
Bailey, DL (2005). Bailey, Dale L; Townsend, David W; Valk, Peter E; Maisey, Michael N (eds.).
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Hamalainen, M.; Hari, Riitta; Ilmoniemi, Risto J.; Knuutila, Jukka; Lounasmaa, Olli V. (1993).
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Stephan, KE; Penny, WD; Moran, RJ; Den Ouden, HE; Daunizeau, J; Friston, KJ (2010).
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Daunizeau, J.; Preuschoff, K; Friston, K; Stephan, K (2011). Sporns, Olaf (ed.).
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853:"Statistical parametric maps in functional imaging: a general linear approach"
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Statistical parametric mapping : the analysis of functional brain image
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Ashburner, J.; Friston, KJ (2000). "Voxel-Based
Morphometry-The Methods".
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Friston, K. (2002). "Functional integration and inference in the brain".
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BΓΌchel, C. (2003). Virginia Ng; Gareth J. Barker; Talma
Hendler (eds.).
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Calhoun, V.; Sui, J; Kiehl, K; Turner, J; Allen, E; Pearlson, G (2011).
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1252:. Bradford Books Series. Cambridge, Mass: MIT Press. pp. 161β186.
791:"Optimizing experimental design for comparing models of brain function"
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Friston, K.; Harrison, L; Penny, W (2003). "Dynamic causal modelling".
1248:
Friston, Karl J. (2004). Kenneth
Hugdahl; Richard J Davidson (eds.).
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1000:"Predicting IQ change from brain structure: a cross-validation study"
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Price, CJ; Ramsden, S; Hope, TM; Friston, KJ; Seghier, ML (2013).
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In functional integration, there is a distinction drawn between
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of functional deficits in patients with damage in this area.
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Study of cooperation of brain regions to process information
1250:
Characterizing
Functional Asymmetries with Brain Mapping
946:
Urner, M.; Schwarzkopf, DS; Friston, K; Rees, G (2013).
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Montague, P.; Dolan, RJ; Friston, KJ; Dayan, P (2012).
1203:The Importance of Connectivity for Brain Function
452:"What we can do and what we cannot do with fMRI"
1299:. Amsterdam; Boston: Elsevier/Academic Press.
742:"Ten simple rules for dynamic causal modeling"
8:
524:Positron Emission Tomography: Basic Sciences
1329:Scholarpedia article on Functional imaging
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255:Changes in resting-state brain activation
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331:Although fMRI studies of people with
260:resting state for up to 24 hours and
68:Functional magnetic resonance imaging
7:
1004:Developmental Cognitive Neuroscience
561:
559:
400:
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568:Journal of Integrative Neuroscience
14:
1295:Friston, K. J. (Karl J.) (2007).
964:10.1016/j.neuroimage.2013.03.050
758:10.1016/j.neuroimage.2009.11.015
373:10.1016/j.neuroimage.2005.08.035
262:dynamic functional connectivity
699:Magnetic Resonance in Medicine
323:phenomena remains mysterious.
179:Statistical parametric mapping
173:Statistical parametric mapping
1:
668:10.1016/S1053-8119(03)00202-7
625:10.1016/s0301-0082(02)00076-x
1155:Trends in Cognitive Sciences
816:10.1371/journal.pcbi.1002280
105:Positron emission tomography
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1167:10.1016/j.tics.2011.11.018
1151:"Computational psychiatry"
795:PLOS Computational Biology
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1016:10.1016/j.dcn.2013.03.001
580:10.1142/S0219635210002512
434:10.1103/RevModPhys.65.413
190:statistically significant
1117:10.3389/fpsyt.2011.00075
603:Progress in Neurobiology
164:Dynamic causal modelling
158:Dynamic causal modelling
1104:Frontiers in Psychiatry
229:Voxel-based morphometry
223:Voxel-based morphometry
917:10.1006/nimg.2000.0582
711:10.1002/mrm.1910390602
298:measurement, however.
219:
154:
90:Magnetoencephalography
20:Functional integration
872:10.1002/hbm.460020402
327:Psychiatric disorders
217:
205:Bonferroni correction
186:each individual voxel
152:
1061:1993Natur.362..342P
860:Human Brain Mapping
807:2011PLSCB...7E2280D
479:10.1038/nature06976
471:2008Natur.453..869L
426:1993RvMP...65..413H
313:supramarginal gyrii
209:random field theory
110:tenths of seconds.
220:
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137:connectivity, and
120:Multimodal imaging
114:Multimodal imaging
43:Imaging techniques
1306:978-0-12-372560-8
541:978-1-84628-007-8
448:Logothetis, N. K.
308:phonological loop
302:Phonological loop
201:linear regression
129:Modes of analysis
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