Advertisement
Review ArticleReview Article
Open Access

Functional and Structural MR Imaging in Neuropsychiatric Disorders, Part 2: Application in Schizophrenia and Autism

S. Mueller, D. Keeser, M.F. Reiser, S. Teipel and T. Meindl
American Journal of Neuroradiology December 2012, 33 (11) 2033-2037; DOI: https://doi.org/10.3174/ajnr.A2800

Abstract

SUMMARY: During the past decade, the application of advanced MR imaging techniques in neuropsychiatric disorders has seen a rapid increase. Disease-specific alterations in brain function can be assessed by fMRI. Structural GM and WM properties are increasingly investigated by DTI and voxel-based approaches like VBM. These methods provide neurobiologic correlates for brain architecture and function, evaluation tools for therapeutic approaches, and potential early markers for diagnosis. Having provided insight into principles of functional and structural imaging and delineated common findings in mild cognitive impairment and Alzheimer disease in Part 1 of this review, we will now focus on autism and schizophrenia as common psychiatric disorders covering different stages of the life span. This review concludes by summarizing current applications, limitations, and future prospects in the field of MR imaging−based neuroimaging.

ABBREVIATIONS:

ACC
anterior cingulate cortex
ASD
autism spectrum disorders
BOLD
blood oxygen level–dependent
DMN
default mode network
DSM
Diagnostic and Statistical Manual of Mental Disorders
FA
fractional anisotropy
GM
gray matter
HFA
high-functioning autism
ICD
International Classification of Diseases
RSN
resting-state network
rsfMRI
resting-state fMRI
VBM
voxel-based morphometry

The past years have seen significant gains in the areas of functional and structural neuroimaging, particularly in their application to the field of neuropsychiatric disorders.

MR imaging−based methods such as fMRI, DTI, and VBM provide neurobiologic correlates of neuropsychiatric disorders in vivo and potential early markers of disease for improved diagnosis. However, we still need to tune the available neuroimaging markers to provide individually applicable markers of disease to predict the risk of asymptomatic or oligosymptomatic subjects developing a specific neuropsychiatric disease. This is most relevant for the design and conduction of future primary or secondary prevention trials of neurodegenerative disorders such as Alzheimer disease or genetically determined disorders the course of which, however, can be dramatically improved through early intervention, such as schizophrenia or ASD. To date, the clinical application of MR imaging in neuropsychiatric diseases is mostly limited to the exclusion of macroscopic abnormalities. Part 1 delineated the methodologic basis and main findings in mild cognitive impairment and Alzheimer disease; this part summarizes major findings gained by applying these techniques to schizophrenia and ASD. We conclude with a discussion of the scope, limitations, and future prospects of functional and structural MR imaging techniques in neuropsychiatric disorders.

Schizophrenia

Schizophrenia is a chronic mental disorder characterized by severe perturbations in cognition, affect, and behavior. Delusions and hallucinations, predominantly auditory in type, are typical symptoms of schizophrenia, but not obligatory. With a lifetime prevalence of approximately 1%, schizophrenia is a relatively common disorder1 that affects most patients in adolescence and early adulthood. This early age of onset and the often severe symptoms account for the profound consequences of the disease for the social and occupational status and the well-being of patients and their families. Clinical and genetic studies have accumulated evidenced for the concept of prodromal stages of psychosis,2 which may offer the chance of early intervention and even prevention of the full manifestation of schizophrenia.3 However, we still lack sensitive and specific biomarkers that may be useful in increasing the accuracy of the presently available preclinical criteria of schizophrenia for future intervention trials. Here, structural and functional imaging may gain an important role in the near future. Imaging studies have described findings in individuals with a (genetic) risk for schizophrenia, relatives of patients with schizophrenia, and patients with first-episode schizophrenia and chronic schizophrenia. In this review, we will concentrate on residual or recurrent schizophrenia because most studies have focused on these stages and the findings obtained in these subjects will set the basis for the neuronal network changes that may occur in prodromal stages of schizophrenia.

Brain Function

Alterations of task-induced brain activation have been described in schizophrenia. Two core findings across studies are a decrease of frontal activations, referred to as hypofrontality, and an increased activation of midline structures such as the ACC, interpreted as resting-state activations that persist inappropriately into task conditions.4⇓–6 Consistently, a meta-analysis of 41 executive-function studies in schizophrenia revealed reduced activation in the left dorsolateral prefrontal cortex, rostral/dorsal ACC, left thalamus, and inferior/posterior cortical areas. Increased activation was observed in several midline cortical areas.7

Investigation of functional connectivity by means of rsfMRI showed decreased connectivity between the bilateral auditory cortex regions in patients with auditory hallucinations.8 Reduced resting-state connectivity between the left temporoparietal junction and the bilateral ACC and amygdala was found to correlate with the severity of auditory-verbal hallucinations.9 Reduced connectivity is furthermore reported between cognitive control networks,10 within the DMN,11 and between the retrosplenial cortex and both the temporal lobe and the DMN12 as well as among the bilateral posterior cingulate cortex, extrastriate cortex, medial prefrontal cortex, and parahippocampal gyrus.13 Although reduced connectivity seems the leading finding in schizophrenia, there are resting-state MR imaging studies reporting not only reduced but also increased connectivity between components or subsystems of the DMN.14⇓–16 For a comprehensive review of both task-dependent as well as task-independent functional connectivity in schizophrenia, also see a recent review by Pettersson-Yeo et al.17 The diagnostic use of these changes in first-episode or even prodromal stages of schizophrenia is still unexplored.

Brain Structure

Besides the reported functional alterations in frontal connectivity, schizophrenia is known to be characterized by mainly left-sided frontal GM volume reduction.18,19 Further regions of decreased GM as detected by VBM include the limbic and paralimbic cortices as well as the thalamus.19,20 VBM studies furthermore revealed WM reductions in schizophrenia. A recent meta-analysis of 17 VBM studies found 4 clusters of consistently reduced WM in schizophrenia compared with healthy controls, which are the bilateral frontal cortices and the bilateral internal capsules.21

Apart from a few exceptions, most DTI studies in schizophrenia have found fractional FA-value reduction in various brain regions.22,23 A recent meta-analysis of 15 DTI studies in schizophrenia counted FA reductions in 112 coordinates and no FA increase. Overall, significant reductions in studies were consistent in 2 regions: the left frontal deep WM and the left temporal deep WM.24 The first region is located within WM tracts connecting the frontal lobe, the thalamus, and the cingulate gyrus. The second region is traversed by WM tracts connecting the frontal lobe, insula, hippocampus-amygdala, and temporal and occipital lobes. Likewise impaired anatomic connections are likely to be accompanied by functional alterations, though to date, evidence about the mutual interaction and temporal sequence of functional and structural changes is still lacking.

Multimodal approaches, investigating WM and GM or structure and function at the same time, might help to elucidate these relationships. A recent study by Pomarol-Clotet at al,25 combining fMRI, VBM, and DTI, found largely overlapping sites of pathologic alterations. Reduced task-induced deactivation of a medial frontal area, including the ACC, was accompanied by GM volume loss in the medial frontal cortex, including the ACC. DTI analysis revealed reduced FA values in the anterior portion of the corpus callosum, and a seed-based tractography showed diminished anatomic connectivity between the anterior corpus callosum and a medial frontal area largely overlapping with regions of decreased task-induced deactivation and decreased GM volume.25 A study applying rsfMRI and DTI tractography to investigate alterations in functional and anatomic connectivity in schizophrenia also found convergent results. Reduced functional connectivity of the bilateral hippocampus to regions including the posterior cingulate cortex and the gyrus parahippocampalis was accompanied by significantly reduced FA values in the fornix body.13 However, other multimodal approaches combining GM VBM and task-dependent fMRI26 or rsfMRI27 did not report such convergent findings.

ASD

ASD are relatively common neurodevelopmental disorders, affecting approximately 1 per 150 children.28 ASD are characterized by 3 core symptoms, namely impaired social reciprocity, communication difficulties, and repetitive stereotyped behaviors. Motor function, attention, and other cognitive domains may also be affected.29 Individuals with learning impairment are subcategorized in the group of low-functioning autism. Individuals without learning impairment but delayed phrase-speech acquisition can be attributed to the group of HFA, whereas individuals who display phrase speech before 36 months are categorized in the group of Asperger syndrome according to criteria of the ICD-10 of the World Health Organization, 1992.30,31 Treatment of ASD despite the severe impact on families is still limited to supportive, behavioral, and symptomatic approaches. No treatments of underlying disease mechanisms are available. In vivo imaging may offer more insight into the neurobiologic basis of the symptoms and their relation to underlying genetic risk factors, eventually paving the way for primary or secondary preventive trials in ASD.

Brain Structure

ASD is known to display early alterations in WM development.32 Overproportional brain growth29 accompanied by mainly frontally increased FA values in young children32 is followed by WM31,33 and GM34,35 volume reduction and FA value decrease in older children, adolescents, and adults with ASD compared to healthy controls.36⇓⇓⇓–40

For example, Brito et al41 found reduced FA values within the corpus callosum, the right corticospinal tract, and the internal capsule and in both pedunculi cerebri of school-aged children with autism. A recent whole-brain study in 13 adult patients with Asperger syndrome revealed reduced FA values within 13 largely bilateral clusters, including the internal capsule; the frontal, parietal, temporal, and occipital lobes; the cingulate gyrus; and the corpus callosum.42 Although decreased FA values seem to be the leading finding in ASD, several studies reported mixed findings of increased and decreased FA values36,43 and other studies demonstrated that normal FA values do not exclude structural alterations. A tractography study of the main limbic pathway in 24 subjects with Asperger syndrome showed an increase in the number of streamlines (representing the tract volume), without detecting any differences in FA values within that same region.44

Brain Function

Task-dependent fMRI studies showed alterations in memory,45 recognition of face expression,46 selective attention,47 cognitive control and executive function,48 self- and other reflection,49 self-representation,50 and motor-response inhibition51 in patients with ASD. Final conclusions were not yet derived from these studies because of the heterogeneity of paradigms as well as the diagnostic entities and age groups included. rsfMRI studies in patients with ASD have suggested that the disease is characterized by alterations in resting-state connectivity. Because of the predominantly reported decrease of connectivity between various brain regions,52⇓⇓–55 ASD is referred to as a (frontal) disconnectivity disorder. However, increased connectivity, especially within seed regions of the DMN, has been reported and correlated with poorer verbal and nonverbal communication54 and more restricted and repetitive behaviors55 in patients with ASD compared with healthy controls.

For an overview of structural and functional MR imaging findings in ASD see On-Line Tables 1 and 2, respectively.

Discussion and Conclusions

So far, neuroimaging methods have just begun to be applied to predict a neuropsychiatric disease in a single subject. Most advances in this respect have been made in the application to at-risk stages of Alzheimer disease, whereas the identification of high-risk subjects with psychosis or the individual prediction of ASD in otherwise characterized at-risk families remains a matter of future research.

One reason might be that neuropsychiatric diagnoses are predominantly grounded on clinical criteria but do not necessarily overlap specific underlying neurobiologic disorders. The question of “disease entities” (ie, the convergence of neurobiology and symptoms of a disease) has been discussed since the days of Pinel and Esquirol in the 18th century. The strong advocacy for the existence of disease entities by Kraepelin56 has found international reception up to the ninth revision of the ICD criteria in the 1980s. Presently, the concept has been widely abandoned for a more pragmatic approach, in which, for the research criteria of ICD-11 and DSM-V, clinical criteria of psychiatric disease will be complemented by biologic markers, among these functional and structural imaging findings. Autism, for example, comprises a large spectrum ranging from low-functioning autism to HFA, with an ongoing discussion as to whether Asperger syndrome is an autonomous entity or a form of HFA. These discussions will probably only be resolved with the advent of imaging evidence if there is a characteristic endophenotype of these and other entities within the autism spectrum as described by imaging and other biologic markers.

Neuropsychiatric study populations are, furthermore, often heterogeneous in terms of duration of illness (which is likely to have an impact on brain structure) and medication. This is of high relevance particularly in the studies on schizophrenia, in which antipsychotics have been suggested to contribute to the pattern of structural and functional brain abnormalities in chronically ill patients. Only a few studies succeed in reaching sufficient sample sizes when enrolling only medication-naïve subjects. One further challenge is to control for the effects of comorbidities, which are common in psychiatric diseases but have often not been systematically addressed. For example, rates of comorbidity between autism and attention deficit/hyperactivity disorder have been reported to range between 14% and 78%.57 To overcome some of these constraints, studies will have to include bigger sample sizes, possibly in a framework of multicenter cooperation, and apply precisely defined diagnostic inclusion and exclusion criteria.

It is also crucial to record systematically possible demographic and other behavioral and substance-related confounders. Neuroimaging measurements are sensitive to constitutional interindividual differences such as genetics, intelligence, or educational level. rsfMRI studies have, furthermore, demonstrated that sex,58,59 age,60 and even the method of instruction61 influence resting-state results. Especially, functional neuroimaging measures may be affected by transient brain and body states, such as arousal, attention, sleep deprivation, sensory processing of irrelevant stimuli, or the effects of substances with impact on the central nervous system. These effects need to be taken into account and, as far as possible, be controlled for when the focus is on disease-related between-group differences.62 For instance, including a RSN that is not expected to differ between groups can provide some confidence that reported differences in RSN connectivity between the 2 groups are meaningful and are not merely dominated by confounders such as medication differences.63

Pitfalls inherent in task-induced fMRI also originate from the BOLD signal intensity being only a coarse proxy of neural activity with a considerable delay between neural activation and measured hemodynamic response. One further concern in this context is that it remains unclear whether the hemodynamic signal intensity is constant throughout the task or whether, after an initial overoxygenation, a steady-state of back-to-normal oxygenation level is re-attained at some point, despite continuing neural activity. It remains, furthermore, largely unclear whether the time course of this hemodynamic response per se might be altered in disease and not (exclusively) neural activation itself. Here, the combination of fMRI with measures of neuronal activity providing high temporal resolution such as electroencephalography studies will provide more insight.64

Resting-state functional connectivity as a task-independent measure of brain intrinsic activity has the potential to make fMRI studies available to a wider range of patients and might thereby greatly increase the clinical utility of fMRI. It has also been argued that many pathologic entities are characterized by complex alterations in intrinsic functional connectivity, rather than by altered activation of distinct brain regions during task conditions, which cannot explain the range of cognitive and affective impairments in neuropsychiatric disorders. This assumption has initiated a shift of research attention towards intrinsic neural circuits that support sensory, cognitive, and emotional processes assessable by rsfMRI. Nonetheless, methods to extract networks of temporally coherent BOLD signals to determine functional connectivity are also applicable and increasingly used to task-condition data.

The combination of different imaging modalities might be able to partially compensate the specific disadvantages of each single imaging method. Combining different imaging techniques might furthermore reveal common sites of pathology, thereby providing pathophysiologic insight, especially into the relationship between functional and structural alterations in disease. Changes in 1 single imaging technique, often also limited to a distinct brain region or structure, often lack effect size for accurate diagnosis. Therefore it is often not sufficient to rely on only 1 parameter. Pooling multimodal imaging information, therefore, appears to be a promising future direction to reach sufficient statistical power for individual clinical application.

This aim of combining different imaging approaches is closely related to a second important recent development: the application of sophisticated multivariate analysis and variable selection methods like support vector machines (eg, to predict a binary outcome like disease−no disease on the basis of an imaging score) or elastic net regression (to train a composite imaging score to best predict the continuous outcome of psychological or genetic test scores). Also, rather than relying on an a priori hypothesis of a brain region that is most likely affected by the disease, it might be more promising to use data-driven approaches (eg, principal component analysis or factor analysis if parameters are not independent) to detect patterns of alterations highly dominated by disease. Such an approach was recently used by Plant et al.65 By applying feature-selection algorithms to VBM data to identify brain regions showing the highest accuracy for the discrimination between patients with AD and healthy controls, a classification accuracy of ≥92% could be reached.

A known statistical challenge, particularly in whole-brain studies, is the correction for multiple comparisons that can be overcome by multivariate methods that compare overall patterns rather than single locations of significant changes. Applying previously defined masks for pattern matching or goodness-of-fit analysis allows the combination of data-driven exploration (to derive a mask of disease-specific patterns) without facing the problems of reduced statistical power of whole-brain comparisons.

In summary, further work is required to overcome the discussed technical limitations to finally make advanced MR imaging techniques applicable in individual patients with psychiatric disorders. Nonetheless, these imaging modalities are, even to date, extremely valuable and have contributed tremendously to our understanding of the pathophysiology underlying neuropsychiatric diseases. Imaging biomarkers furthermore provide a tool for the evaluation of therapy effects. For a comprehensive review of imaging biomarkers in Alzheimer disease, for instance, see Hampel et al.66

Genetic imaging is an additional emerging field of research in which advanced MR imaging techniques prove acutely valuable. An increasing number of clinical-association studies that compare genetic data from patients and controls identify a growing body of novel genetic risk variants for several mental disorders, including Alzheimer disease,67 schizophrenia,68 and autism.69 Using disease-specific imaging patterns as intermediate phenotypes provides a tool to investigate the impact of these polymorphisms on brain function and structure, thereby furthering insight into how they might influence behavior. These intermediate phenotypes based on direct observation of brain functional and structural architecture are supposed to be closer to the genetic substrate than are clinically diagnosed disorders,70 also because they avoid the above-mentioned heterogeneity and arbitrary boundaries inherent in DSM-IV diagnostic categories. Characterizing the effects of risk genes and variations on brain function and structure as a resulting intermediate phenotype is, therefore, crucial for transforming genomic advances into a pathophysiologic understanding of psychiatric disorders that can inform the development of more effective tools for the treatment and prevention of psychiatric disorders.

Footnotes

  • Disclosures: Stefan Teipel—UNRELATED: Grants/Grants Pending: Janssen-Cilag, Comments: grant provided from 2005 to 2009.

Indicates open access to non-subscribers at www.ajnr.org

References

  1. 1.↵
    1. Perala J,
    2. Suvisaari J,
    3. Saarni SI,
    4. et al
    . Lifetime prevalence of psychotic and bipolar I disorders in a general population. Arch Gen Psychiatry 2007;64:19–28
    CrossRefPubMed
  2. 2.↵
    1. Ruhrmann S,
    2. Schultze-Lutter F,
    3. Salokangas RK,
    4. et al
    . Prediction of psychosis in adolescents and young adults at high risk: results from the prospective European prediction of psychosis study. Arch Gen Psychiatry 2011;67:241–51
  3. 3.↵
    1. Larson MK,
    2. Walker EF,
    3. Compton MT
    . Early signs, diagnosis and therapeutics of the prodromal phase of schizophrenia and related psychotic disorders. Expert Rev Neurother 2020;10:1347–59
  4. 4.↵
    1. Kim DI,
    2. Manoach DS,
    3. Mathalon DH,
    4. et al
    . Dysregulation of working memory and default-mode networks in schizophrenia using independent component analysis, an fBIRN and MCIC study. Human Brain Mapp 2009;30:3795–811
    CrossRefPubMed
  5. 5.↵
    1. Pomarol-Clotet E,
    2. Salvador R,
    3. Sarró S,
    4. et al
    . Failure to deactivate in the prefrontal cortex in schizophrenia: dysfunction of the default mode network? Psychol Med 2008;38:1185–93
    PubMed
  6. 6.↵
    1. Glahn DC,
    2. Ragland JD,
    3. Abramoff A,
    4. et al
    . Beyond hypofrontality: a quantitative meta-analysis of functional neuroimaging studies of working memory in schizophrenia. Hum Brain Mapp 2005;25:60–69
    CrossRefPubMed
  7. 7.↵
    1. Minzenberg MJ,
    2. Laird AR,
    3. Thelen S,
    4. et al
    . Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch Gen Psychiatry 2009;66:811–22
    CrossRefPubMed
  8. 8.↵
    1. Gavrilescu M,
    2. Rossell S,
    3. Stuart GW,
    4. et al
    . Reduced connectivity of the auditory cortex in patients with auditory hallucinations: a resting state functional magnetic resonance imaging study. Psychol Med 2010;40:1149–58
    CrossRefPubMed
  9. 9.↵
    1. Vercammen A,
    2. Knegtering H,
    3. den Boer JA,
    4. et al
    . Auditory hallucinations in schizophrenia are associated with reduced functional connectivity of the temporo-parietal area. Biol Psychiatry 2010;67:912–18
    CrossRefPubMed
  10. 10.↵
    1. Repovs G,
    2. Csernansky JG,
    3. Barch DM
    . Brain network connectivity in individuals with schizophrenia and their siblings. Biol Psychiatry 2011;69:967–73. Epub 2010 Dec 30
    CrossRefPubMed
  11. 11.↵
    1. Camchong J,
    2. Macdonald AW 3rd,.,
    3. Bell C,
    4. et al.
    Altered functional and anatomical connectivity in schizophrenia. Schizophr Bull 2011;37:640–50. Epub 2009 Nov 17
    Abstract/FREE Full Text
  12. 12.↵
    1. Bluhm RL,
    2. Miller J,
    3. Lanius RA,
    4. et al
    . Retrosplenial cortex connectivity in schizophrenia. Psychiatry Res 2009;174:17–23. Epub 2009 Sep 23
    CrossRefPubMed
  13. 13.↵
    1. Zhou Y,
    2. Shu N,
    3. Liu Y,
    4. et al
    . Altered resting-state functional connectivity and anatomical connectivity of hippocampus in schizophrenia. Schizophrenia Res 2008;100:120–32
    CrossRefPubMed
  14. 14.↵
    1. Ongur D,
    2. Lundy M,
    3. Greenhouse I,
    4. et al
    . Default mode network abnormalities in bipolar disorder and schizophrenia. Psychiatry Res 2010;183:59–68
    CrossRefPubMed
  15. 15.↵
    1. Rotarska-Jagiela A,
    2. van de Ven V,
    3. Oertel-Knöchel V,
    4. et al
    . Resting-state functional network correlates of psychotic symptoms in schizophrenia. Schizophrenia Res 2010;117:21–30
    CrossRefPubMed
  16. 16.↵
    1. Skudlarski P,
    2. Jagannathan K,
    3. Anderson K,
    4. et al
    . Brain connectivity is not only lower but different in schizophrenia: a combined anatomical and functional approach. Biol Psychiatry 2010;68:61–69
    CrossRefPubMed
  17. 17.↵
    1. Pettersson-Yeo W,
    2. Allen P,
    3. Benetti S,
    4. et al
    . Dysconnectivity in schizophrenia: where are we now? Neurosci Biobehav Rev 2011;35:1110–24. Epub 2010 Nov 27
    CrossRefPubMed
  18. 18.↵
    1. Fornito A,
    2. Yücel M,
    3. Patti J,
    4. et al
    . Mapping gray matter reductions in schizophrenia: an anatomical likelihood estimation analysis of voxel-based morphometry studies. Schizophrenia Res 2009;108:104–13
    CrossRefPubMed
  19. 19.↵
    1. Glahn DC,
    2. Laird AR,
    3. Ellison-Wright I,
    4. et al
    . Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biol Psychiatry 2008;64:774–81
    CrossRefPubMed
  20. 20.↵
    1. Ellison-Wright I,
    2. Glahn DC,
    3. Laird AR,
    4. et al
    . The anatomy of first-episode and chronic schizophrenia: an anatomical likelihood estimation meta-analysis. Am J Psychiatry 2008;165:1015–23
    CrossRefPubMed
  21. 21.↵
    1. Di X,
    2. Chan RCK,
    3. Gong Q-y
    . White matter reduction in patients with schizophrenia as revealed by voxel-based morphometry: an activation likelihood estimation meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry 2009;33:1390–94. Epub 2009 Sep 7
    CrossRefPubMed
  22. 22.↵
    1. Kubicki M,
    2. McCarley R,
    3. Westin CF,
    4. et al
    . A review of diffusion tensor imaging studies in schizophrenia. J Psychiatr Res 2007;41:15–30
    CrossRefPubMed
  23. 23.↵
    1. Buchsbaum MS,
    2. Friedman J,
    3. Buchsbaum BR,
    4. et al
    . Diffusion tensor imaging in schizophrenia. Biol Psychiatry 2006;60:1181–87
    CrossRefPubMed
  24. 24.↵
    1. Ellison-Wright I,
    2. Bullmore E
    . Meta-analysis of diffusion tensor imaging studies in schizophrenia. Schizophr Res 2009;108:3–10
    CrossRefPubMed
  25. 25.↵
    1. Pomarol-Clotet E,
    2. Canales-Rodriguez EJ,
    3. Salvador R,
    4. et al
    . Medial prefrontal cortex pathology in schizophrenia as revealed by convergent findings from multimodal imaging. Mol Psychiatry 2010;15:823–30
    CrossRefPubMed
  26. 26.↵
    1. Calhoun VD,
    2. Adali T,
    3. Giuliani NR,
    4. et al
    . Method for multimodal analysis of independent source differences in schizophrenia: combining gray matter structural and auditory oddball functional data. Hum Brain Mapp 2006;27:47–62
    CrossRefPubMed
  27. 27.↵
    1. Lui S,
    2. Deng W,
    3. Huang X,
    4. et al
    . Association of cerebral deficits with clinical symptoms in antipsychotic-naive first-episode schizophrenia: an optimized voxel-based morphometry and resting state functional connectivity study. Am J Psychiatry 2009;166:196–205
    CrossRefPubMed
  28. 28.↵
    1. Rapin I,
    2. Tuchman RF
    . What is new in autism? Curr Opin Neurol 2008;21:143–49
    CrossRefPubMed
  29. 29.↵
    1. Courchesne E,
    2. Redcay E,
    3. Kennedy DP
    . The autistic brain: birth through adulthood. Curr Opin Neurol 2004;17:489–96
    CrossRefPubMed
  30. 30.↵
    1. Howlin P
    . Outcome in high-functioning adults with autism with and without early language delays: implications for the differentiation between autism and Asperger syndrome. J Autism Develop Disord 2003;33:3–13
    CrossRef
  31. 31.↵
    1. McAlonan GM,
    2. Cheung C,
    3. Cheung V,
    4. et al
    . Differential effects on white-matter systems in high-functioning autism and Asperger's syndrome. Psychol Med 2009;39:1885–93
    CrossRefPubMed
  32. 32.↵
    1. Ben Bashat D,
    2. Kronfeld-Duenias V,
    3. Zachor DA,
    4. et al
    . Accelerated maturation of white matter in young children with autism: a high b value DWI study. Neuroimage 2007;37:40–47
    CrossRefPubMed
  33. 33.↵
    1. Toal F,
    2. Daly EM,
    3. Page L,
    4. et al
    . Clinical and anatomical heterogeneity in autistic spectrum disorder: a structural MRI study. Psychol Med 2010;40:1171–81
    CrossRefPubMed
  34. 34.↵
    1. Kwon H,
    2. Ow AW,
    3. Pedatella KE,
    4. et al
    . Voxel-based morphometry elucidates structural neuroanatomy of high-functioning autism and Asperger syndrome. Develop Med Child Neurol 2004;46:760–64
    CrossRefPubMed
  35. 35.↵
    1. McAlonan GM,
    2. Suckling J,
    3. Wong N,
    4. et al
    . Distinct patterns of gray matter abnormality in high-functioning autism and Asperger's syndrome. J Child Psychol Psychiatry 2008;49:1287–95
    CrossRefPubMed
  36. 36.↵
    1. Barnea-Goraly N,
    2. Kwon H,
    3. Menon V,
    4. et al
    . White matter structure in autism: preliminary evidence from diffusion tensor imaging. Biol Psychiatry 2004;55:323–26
    CrossRefPubMed
  37. 37.↵
    1. Keller TA,
    2. Kana RK,
    3. Just MA
    . A developmental study of the structural integrity of white matter in autism. Neuroreport 2007;18:23–27
    CrossRefPubMed
  38. 38.↵
    1. Lee JE,
    2. Bigler ED,
    3. Alexander AL,
    4. et al
    . Diffusion tensor imaging of white matter in the superior temporal gyrus and temporal stem in autism. Neurosci Lett 2007;424:127–32
    CrossRefPubMed
  39. 39.↵
    1. Thakkar KN,
    2. Polli FE,
    3. Joseph RM,
    4. et al
    . Response monitoring, repetitive behaviour and anterior cingulate abnormalities in autism spectrum disorders (ASD). Brain 2008;131:2464–78
    Abstract/FREE Full Text
  40. 40.↵
    1. Alexander AL,
    2. Lee JE,
    3. Lazar M,
    4. et al
    . Diffusion tensor imaging of the corpus callosum in autism. Neuroimage 2007;34:61–73
    CrossRefPubMed
  41. 41.↵
    1. Brito AR,
    2. Vasconcelos MM,
    3. Domingues RC,
    4. et al
    . Diffusion tensor imaging findings in school-aged autistic children. J Neuroimaging 2009;19:337–43
    CrossRefPubMed
  42. 42.↵
    1. Bloemen OJ,
    2. Deeley Q,
    3. Sundram F,
    4. et al
    . White matter integrity in Asperger syndrome: a preliminary diffusion tensor magnetic resonance imaging study in adults. Autism Res 2010;3:203–13
    CrossRefPubMed
  43. 43.↵
    1. Cheng Y,
    2. Chou KH,
    3. Chen IY,
    4. et al
    . Atypical development of white matter microstructure in adolescents with autism spectrum disorders. Neuroimage 2010;50:873–82
    CrossRefPubMed
  44. 44.↵
    1. Pugliese L,
    2. Catani M,
    3. Arneis S,
    4. et al
    . The anatomy of extended limbic pathways in Asperger syndrome: a preliminary diffusion tensor imaging tractography study. Neuroimage 2009;47:427–34
    CrossRefPubMed
  45. 45.↵
    1. Noonan SK,
    2. Haist F,
    3. Müller RA
    . Aberrant functional connectivity in autism: evidence from low-frequency BOLD signal fluctuations. Brain Res 2009;1262:48–63
    CrossRefPubMed
  46. 46.↵
    1. Welchew DE,
    2. Ashwin C,
    3. Berkouk K,
    4. et al
    . Functional disconnectivity of the medial temporal lobe in Asperger's syndrome. Biol Psychiatry 2005;57:991–98
    CrossRefPubMed
  47. 47.↵
    1. Belmonte MK,
    2. Yurgelun-Todd DA
    . Functional anatomy of impaired selective attention and compensatory processing in autism. Cogn Brain Res 2003;17:651–64
    CrossRefPubMed
  48. 48.↵
    1. Solomon M,
    2. Ozonoff SJ,
    3. Ursu S,
    4. et al
    . The neural substrates of cognitive control deficits in autism spectrum disorders. Neuropsychologia 2009;47:2515–26
    CrossRefPubMed
  49. 49.↵
    1. Kennedy DP,
    2. Courchesne E
    . Functional abnormalities of the default network during self- and other-reflection in autism. Soc Cogn Affect Neurosci 2008;3:177–90
    Abstract/FREE Full Text
  50. 50.↵
    1. Lombardo MV,
    2. Chakrabarti B,
    3. Bullmore ET,
    4. et al
    . Atypical neural self-representation in autism. Brain 2010;133:611–24
    Abstract/FREE Full Text
  51. 51.↵
    1. Lee PS,
    2. Yerys BE,
    3. Della Rosa A,
    4. et al
    . Functional connectivity of the inferior frontal cortex changes with age in children with autism spectrum disorders: a fcMRI study of response inhibition. Cereb Cortex 2009;19:1787–94
    Abstract/FREE Full Text
  52. 52.↵
    1. Cherkassky VL,
    2. Kana RK,
    3. Keller TA,
    4. Just MA
    . Functional connectivity in a baseline resting-state network in autism. Neuroreport 2006;17:1687–90
    CrossRefPubMed
  53. 53.↵
    1. Kennedy DP,
    2. Courchesne E
    . The intrinsic functional organization of the brain is altered in autism. Neuroimage 2008;39:1877–85
    CrossRefPubMed
  54. 54.↵
    1. Weng SJ,
    2. Wiggins JL,
    3. Peltier SJ,
    4. et al
    . Alterations of resting state functional connectivity in the default network in adolescents with autism spectrum disorders. Brain Res 2009;1313:202–14
    CrossRefPubMed
  55. 55.↵
    1. Monk CS,
    2. Peltier SJ,
    3. Wiggins JL,
    4. et al
    . Abnormalities of intrinsic functional connectivity in autism spectrum disorders. Neuroimage 2009;47:764–72
    CrossRefPubMed
  56. 56.↵
    1. Kraepelin E
    . Klinische Psychiatrie. Leipzig, Germany: Barth; 1910
  57. 57.↵
    1. Gargaro BA,
    2. Rinehart NJ,
    3. Bradshaw JL,
    4. et al
    . Autism and ADHD: how far have we come in the comorbidity debate? Neurosci Biobehav Rev 2011;35:1081–88
    CrossRefPubMed
  58. 58.↵
    1. Tomasi D,
    2. Volkow ND
    . Gender differences in brain functional connectivity density. Hum Brain Mapp 2012;33:849–60
    CrossRefPubMed
  59. 59.↵
    1. Tian L,
    2. Wang J,
    3. Yan C,
    4. He Y
    . Hemisphere- and gender-related differences in small-world brain networks: a resting-state functional MRI study. Neuroimage 2011;54:191–202
    CrossRefPubMed
  60. 60.↵
    1. Madden DJ,
    2. Costello MC,
    3. Dennis NA,
    4. et al
    . Adult age differences in functional connectivity during executive control. Neuroimage 2010;52:643–57
    CrossRefPubMed
  61. 61.↵
    1. Benjamin C,
    2. Lieberman DA,
    3. Chang M,
    4. et al
    . The influence of rest period instructions on the default mode network. Front Hum Neurosci 2010;4:218
    PubMed
  62. 62.↵
    1. Dickerson BC,
    2. Sperling RA
    . Large-scale functional brain network abnormalities in Alzheimer's disease: insights from functional neuroimaging. Behav Neurol 2009;21:63–75
    CrossRefPubMed
  63. 63.↵
    1. Sorg C,
    2. Riedl V,
    3. Muhlau M,
    4. et al
    . Selective changes of resting-state networks in individuals at risk for Alzheimer's disease. Proc Natl Acad Sci U S A 2007;104:18760–65
    Abstract/FREE Full Text
  64. 64.↵
    1. Mulert C,
    2. Pogarell O,
    3. Hegerl U
    . Simultaneous EEG-fMRI: perspectives in psychiatry. Clin EEG Neurosci 2008;39:61–64
    Abstract/FREE Full Text
  65. 65.↵
    1. Plant C,
    2. Teipel SJ,
    3. Oswald A,
    4. et al
    . Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease. Neuroimage 2010;50:162–74
    CrossRefPubMed
  66. 66.↵
    1. Hampel H,
    2. Frank R,
    3. Broich K,
    4. et al
    . Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives. Nat Rev Drug Discov 2010;9:560–74
    CrossRefPubMed
  67. 67.↵
    1. Harold D,
    2. Abraham R,
    3. Hollingworth P,
    4. et al
    . Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease. Nat Genet 2009;41:1088–93
    CrossRefPubMed
  68. 68.↵
    1. O'Donovan MC,
    2. Craddock N,
    3. Norton N,
    4. et al
    . Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet 2008;40:1053–55
    CrossRefPubMed
  69. 69.↵
    1. Weiss LA,
    2. Arking DE,
    3. Daly MJ,
    4. et al
    . A genome-wide linkage and association scan reveals novel loci for autism. Nature 2009;461:802–08
    CrossRefPubMed
  70. 70.↵
    1. Gottesman II,
    2. Gould TD
    . The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry 2003;160:636–45
    CrossRefPubMed
View Abstract
Back to top

In this issue

Advertisement
Print
Download PDF
Email Article
Cite this article
Bookmark this article
Functional and Structural MR Imaging in Neuropsychiatric Disorders, Part 2: Application in Schizophrenia and Autism
S. Mueller, D. Keeser, M.F. Reiser, S. Teipel, T. Meindl
American Journal of Neuroradiology Dec 2012, 33 (11) 2033-2037; DOI: 10.3174/ajnr.A2800
del.icio.us logo Twitter logo Facebook logo Mendeley logo
Purchase

Jump to section

Related Articles

Cited By...

More in this TOC Section

Similar Articles

Advertisement