|
1
|
Belmonte MK, Allen G, Beckel-Mitchener A,
Boulanger LM, Carper RA and Webb SJ: Autism and abnormal
development of brain connectivity. J Neurosci. 24:9228–9231. 2004.
View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Developmental Disabilities Monitoring
Network Surveillance Year 2010 Principal Investigators, . Centers
for Disease Control and Prevention (CDC): Prevalence of autism
spectrum disorder among children aged 8 years-autism and
developmental disabilities monitoring network, 11 sites, United
States, 2010. MMWR Surveill Summ. 63:1–22. 2014.
|
|
3
|
McCarthy M: Autism diagnoses in the US
rise by 30%, CDC reports. BMJ. 348:g25202014. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Experts, . Large Numbers of children under
3 with autism go undiagnosed. http://china.caixin.com/2018-10-25/101338630.htmlOctober
25–2018
|
|
5
|
Xiao Z, Wang C, Nan J and Wu J: SAE-based
classification of school-aged children with autism spectrum
disorders using functional magnetic resonance imaging. Multimed
Tools Applicat. 77:22809–22820. 2018. View Article : Google Scholar
|
|
6
|
Holzer L, Mihailescu R, Rodrigues-Degaeff
C, Junier L, Muller-Nix C, Halfon O and Ansermet F: Community
introduction of practice parameters for autistic spectrum
disorders: Advancing early recognition. J Autism Dev Disord.
36:249–262. 2006. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Goh S, Dong Z, Zhang Y, DiMauro S and
Peterson BS: Mitochondrial dysfunction as a neurobiological subtype
of autism spectrum disorder: Evidence from brain imaging. JAMA
Psychiatry. 71:665–671. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Lord C, Rutter M and Le Couteur A: Autism
Diagnostic Interview-Revised: A revised version of a diagnostic
interview for caregivers of individuals with possible pervasive
developmental disorders. J Autism Dev Disord. 24:659–685. 1994.
View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Bastiaansen JA, Meffert H, Huizinga P,
Ketelaars C, Pijnenborg M, Bartels A, Minderaa R, Keysers C and de
Bildt A: Diagnosing Autism Spectrum Disorders in Adults: the Use of
Autism Diagnostic Observation Schedule (ADOS) Module 4. J Autism
Dev Disord 41 (9). 1256–1266. 2011. View Article : Google Scholar
|
|
10
|
Sungur MZ and Gündüz A: A comparison of
DSM-IV-TR and DSM-5 definitions for sexual dysfunctions: Critiques
and challenges. J Sex Med. 11:364–373. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Dosreis S, Weiner CL, Johnson L and
Newschaffer CJ: Autism spectrum disorder screening and management
practices among general pediatric providers. J Dev Behav Pediatr.
27:S88–S94. 2006. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Matson JL and Goldin RL: Diagnosing young
children with autism. Int J Dev Neurosci. 39:44–48. 2014.
View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Di Martino A, Ghaffari M, Curchack J,
Reiss P, Hyde C, Vannucci M, Petkova E, Klein DF and Castellanos
FX: Decomposing intra-subject variability in children with
attention-deficit/hyperactivity disorder. Biol Psychiatry.
64:607–614. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Hoptman MJ, Zuo XN, Butler PD, Javitt DC,
D'Angelo D, Mauro CJ and Milham MP: Amplitude of low-frequency
oscillations in schizophrenia: A resting state fMRI study.
Schizophr Res. 117:13–20. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Han Y, Wang J, Zhao Z, Min B, Lu J, Li K,
He Y and Jia J: Frequency-dependent changes in the amplitude of
low-frequency fluctuations in amnestic mild cognitive impairment: A
resting-state fMRI study. Neuroimage. 55:287–295. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Barttfeld P, Wicker B, Cukier S, Navarta
S, Lew S, Leiguarda R and Sigman M: State-dependent changes of
connectivity patterns and functional brain network topology in
autism spectrum disorder. Neuropsychologia. 50:3653–3662. 2012.
View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Bajaj S, Adhikari BM and Dhamala M: Higher
frequency network activity flow predicts lower frequency node
activity in intrinsic low-frequency BOLD fluctuations. PLoS One.
8:e644662013. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Deshpande G, Libero LE, Sreenivasan KR,
Deshpande HD and Kana RK: Identification of neural connectivity
signatures of autism using machine learning. Front Hum Neurosci.
7:6702013. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Chen H, Duan X, Liu F, Lu F, Ma X, Zhang
Y, Uddin LQ and Chen H: Multivariate classification of autism
spectrum disorder using frequency-specific resting-state functional
connectivity-A multi-center study. Prog Neuropsychopharmacol Biol
Psychiatry. 64:1–9. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Zuo XN, Di Martino A, Kelly C, Shehzad ZE,
Gee DG, Klein DF, Castellanos FX, Biswal BB and Milham MP: The
oscillating brain: Complex and reliable. Neuroimage. 49:1432–1445.
2010. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Penttonen M and Buzsáki G: Natural
logarithmic relationship between brain oscillators. Thalamus Relat
Syst. 2:145–152. 2003. View Article : Google Scholar
|
|
22
|
Buzsáki G and Draguhn A: Neuronal
oscillations in cortical networks. Science. 304:1926–1929. 2004.
View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Siegel M, Donner TH and Engel AK: Spectral
fingerprints of large-scale neuronal interactions. Nat Rev
Neurosci. 13:121–134. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Lynch CJ, Uddin LQ, Supekar K, Khouzam A,
Phillips J and Menon V: Default mode network in childhood autism:
Posteromedial cortex heterogeneity and relationship with social
deficits. Biol Psychiatry. 74:212–219. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Balsters JH, Mantini D and Wenderoth N:
Connectivity-based parcellation reveals distinct cortico-striatal
connectivity fingerprints in Autism Spectrum Disorder. Neuroimage.
170:412–423. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Risk BB, Matteson DS, Ruppert D, Eloyan A
and Caffo BS: An evaluation of independent component analyses with
an application to resting-state fMRI. Biometrics. 70:224–236. 2014.
View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Shin HC, Orton MR, Collins DJ, Doran SJ
and Leach MO: Stacked autoencoders for unsupervised feature
learning and multiple organ detection in a pilot study using 4D
patient data. IEEE Trans Pattern Anal Mach Intell. 35:1930–1943.
2013. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Autism Brain Imaging Data Exchange II,
2016, . http://fcon_1000.projects.nitrc.org/indi/abide/abide_II.htmlAug
30–2012
|
|
29
|
Assaf M, Jagannathan K, Calhoun VD, Miller
L, Stevens MC, Sahl R, O'Boyle JG, Schultz RT and Pearlson GD:
Abnormal functional connectivity of default mode sub-networks in
autism spectrum disorder patients. Neuroimage. 53:247–256. 2010.
View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Di Martino A, Kelly C, Grzadzinski R, Zuo
XN, Mennes M, Mairena MA, Lord C, Castellanos FX and Milham MP:
Aberrant striatal functional connectivity in children with autism.
Biol Psychiatry. 69:847–856. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Perez Velazquez JL, Barcelo F, Hung Y,
Leshchenko Y, Nenadovic V, Belkas J, Raghavan V, Brian J and Garcia
Dominguez L: Decreased brain coordinated activity in autism
spectrum disorders during executive tasks: Reduced long-range
synchronization in the fronto-parietal networks. Int J
Psychophysiol. 73:341–349. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Fagiolo G, Waldman A and Hajnal JV: A
simple procedure to improve FMRIB Software Library Brain Extraction
Tool performance. Br J Radiol 81 (963). 2502008. View Article : Google Scholar
|
|
33
|
Uddin LQ, Supekar K, Lynch CJ, Khouzam A,
Phillips J, Feinstein C, Ryali S and Menon V: Salience
network-based classification and prediction of symptom severity in
children with autism. JAMA Psychiatry. 70:869–879. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Li YO, Adali T and Calhoun VD: Estimating
the number of independent components for functional magnetic
resonance imaging data. Hum Brain Mapp. 28:1251–1266. 2007.
View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Beckmann CF, Noble JA and Smith SM:
Investigating the intrinsic dimensionality of FMRI data for ICA.
Neuroimage. 13:S762001. View Article : Google Scholar
|
|
36
|
McKeown MJ and Sejnowski TJ: Independent
component analysis of fMRI data: Examining the assumptions. Hum
Brain Mapp. 6:368–372. 1998. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Jiang M, Liang Y, Feng X, Fan X, Pei Z,
Xue Y and Guan R: Text classification based on deep belief network
and softmax regression. Neural Comput Applicat. 29:61–70. 2018.
View Article : Google Scholar
|
|
38
|
Anderson JS, Nielsen JA, Froehlich AL,
DuBray MB, Druzgal TJ, Cariello AN, Cooperrider JR, Zielinski BA,
Ravichandran C, Fletcher PT, et al: Functional connectivity
magnetic resonance imaging classification of autism. Brain.
134:3742–3754. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Murdaugh DL, Shinkareva SV, Deshpande HR,
Wang J, Pennick MR and Kana RK: Differential deactivation during
mentalizing and classification of autism based on default mode
network connectivity. PLoS One. 7:e500642012. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Iidaka T: Resting state functional
magnetic resonance imaging and neural network classified autism and
control. Cortex. 63:55–67. 2015. View Article : Google Scholar : PubMed/NCBI
|
